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252 results for “Synthetic data”
Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization"
<p>Synthetic data set "Synth1" for the paper "Adaptive Sampling of 3D Spatial Correlations for Focus+Context Visualization".<br>Preprint of the paper available at: <a href="https://arxiv.org/abs/2309.03308">https://arxiv.org/abs/2309.03308</a></p>
Synthetic COVID-19 Case Reporting Data Generated from an Agent-Based Simulation Model
<p>This is a synthetic case reporting data set for the SARS-CoV-2 epidemic in Austria. The data set statistically reproduces and synthetically augments data on reported cases and was generated with an agent-based simulation model. References to descriptions of the model and the parameterization used to generate the data set is included in the attached PDF file. The data format is described in the README file.</p>
I-BiDaaS - TID - Synthetic Call Centre Data
<p>This is a simulated dataset based on both real phone interactions and conversations usually performed by real call centres. It is comprised of several simulated customer interactions with an agent representative, both roles performed by actors. Phone call recordings are performed using different mobile and landline devices. The scripting, both from customer and agent, aims to develop typical scenarios by telco-oriented call centre operations. Both raw waveform recordings and speech transcription are provided. The latter as obtained by an automatic speech recognition (ASR) prototype developed by TID. The word segmentation timestamps are also provided for those recognized. Additionally, a confidence score is also provided per token basis.</p>
cigKast: A data of 3D synthetic seismic volumes with labeled paleokarsts for deep-learning-based paleokarst interpretation
<p>cigKarst is a dataset created by the <a href="http://cig.ustc.edu.cn/">Computational Interpretation Group (CIG)</a> for the deep-learning-based peleokarst interpretation in 3D seismic images, <a href="http://cig.ustc.edu.cn/xinming/list.htm" target="_blank" rel="noopener">Xinming Wu</a> is the main contributor to the dataset.</p> <p>This dataset contains 120 pairs of synthetic 3D seismic images and the corresponding label images with the ground truth of the paleokarst systems simulated in the seismic images. More detail of building this dataset is discussed in the paper published at the journal of JGR Solid Earth:</p> <p><strong>Wu, X.</strong>, S. Yan, J. Qi, and H. Zeng, 2020, Deep learning for characterizing paleokarst collapse features in 3D seismic images. <strong>JGR, Solid Earth</strong>, Vol. 125(9), 1-23, e2020JB019685. <a href="http://cig.ustc.edu.cn/_upload/tpl/05/cd/1485/template1485/papers/wu2020karst.pdf">[PDF]</a>. doi: 10.1029/2020JB019685</p> <p>Below are some brief description of the dataset:</p> <p>1) The "seismic.zip" contains 120 3D seismic images, each image is with the dimension of 256X256X256;</p> <p> 2) The "karst.zip" contains 120 3D label images of the karsts. Each label image is with the same dimension of 256X256X256. The values in a label image are set with ones in the karst areas while zeros elsewhere, which is why the compressed label images in the karst.zip is much smaller than the seismic images compressed in the seismic.zip</p>
Synthetic Smart Card Data for the Analysis of Temporal and Spatial Patterns
<p>This is a synthetic smart card data set that can be used to test pattern detection methods for the extraction of temporal and spatial data. The data set is tab seperated and based on a stylized travel pattern description for city of Utrecht in The Netherlands and is developed and used in Chapter 6 of the PhD Thesis of Paul Bouman. </p> <p>This dataset contains the following files:</p> <ul> <li>journeys.tsv : the actual data set of synthetic smart card data</li> <li>utrecht.xml : the activity pattern definition that was used to randomly generate the synthethic smart card data</li> <li>validate.ref : a file derived from the activity pattern definition that can be used for validation purposes. It specifies which activity types occur at each location in the smart card data set.</li> </ul>
Synthetic Hyperelastic data for DDI
<p>Synthetic data used in the case study (section 3) of:</p><p>Marie Dalémat, Michel Coret, Adrien Leygue, Erwan Verron. Robustness of the Data-Driven Identification algorithm with incomplete input data. 2021. <a href="https://hal.science/hal-03028848v3">⟨hal-03028848v3⟩</a> </p><p>The data in XDMF + hdf5 format comprises:</p><p>1-the 2D computational mesh with triangular linear elements,</p><p>2-the nodal Forces for all loading steps (nodal quantity),</p><p>3-the displacement for all loading steps (nodal quantity),</p><p>4- Cauchy stress fields for all loading steps (cell quantity). </p><p> </p>
Primary NMR Data Supporting the Article "Synthetic approach to 2-alkyl-4-quinolones and 2-alkyl-4-quinolone-3-carboxamides based on common β-keto amide precursors"
<p>This archive contains raw 1H/13C FIDs and associated data in Bruker-specific format that can be viewed with Bruker’s TopSpin or other appropriate NMR processing software. The subfolders are named in accordance with the compound numbering in the associated research paper (Synthetic Approach to 2-Alkyl-4-quinolones and 2-Alkyl-4-quinolone-3-carboxamides Based on Common β-Keto Amide Precursors).</p> <p>Correspondence: angelov@uni-plovdiv.bg</p> <p> </p>
A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS - supporting data
<p>This dataset contains all data related to "A step towards understanding plastic complexity: antimony speciation in consumer plastics and synthetic textiles revealed by XAS"</p>
Synthetic data (Part 2) for HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the rendered images and the segmentation masks that we use to train our model on HO3Dv2 dataset. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the rendered images for HO3Dv2.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Synthetic data (Part 1) for "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields"
<p>#############</p> <p>HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields, CVPR 2024</p> <p>#############</p> <p>Haozhe Qi, Chen Zhao, Mathieu Salzmann, Alexander Mathis.</p> <p>Affiliation: EPFL</p> <p>Date: June, 2024</p> <p>Link to the CVPR article: https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_HOISDF_Constraining_3D_Hand-Object_Pose_Estimation_with_Global_Signed_Distance_CVPR_2024_paper.pdf</p> <p>Link to the Arxiv article: https://arxiv.org/abs/2402.17062</p> <p>--------------------------------</p> <div> <div>Here we provide the data of our article "HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields". It contains the preprocessed SDF samples. Meanwhile, we also include rendered data for HO3Dv2 here. </div> <br> <div>The overall structure of the data is:</div> <br> <div>├── <a href="../api/records/13228003/draft/files/render_sdf_ho3d.zip/content" target="_blank" rel="noopener noreferrer">render_sdf_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 rendered images.</div> <div>├── <a href="../api/records/13228003/draft/files/train_ho3d.zip/content" target="_blank" rel="noopener noreferrer">train_ho3d.zip</a> - Contains the processed SDF files for HO3Dv2 training set.</div> <div>├── <a href="../api/records/13228003/draft/files/full_test_dexycb.zip/content" target="_blank" rel="noopener noreferrer">full_test_dexycb.zip</a> - Contains the processed SDF files for DexYCB full test set.</div> <div> </div> <br> <div>The code to reproduce the results is available at: https://github.com/amathislab/HOISDF</div> <div> </div> <div>--------------------------------</div> </div> <p>If you find our code, weights, predictions or ideas useful, please cite:</p> <p>@inproceedings{qi2024hoisdf,<br> title={HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields},<br> author={Qi, Haozhe and Zhao, Chen and Salzmann, Mathieu and Mathis, Alexander},<br> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},<br> pages={10392--10402},<br> year={2024}<br>}</p>
Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data
<p>This dataset contains the latest version of a selection of result data of the paper "Mapping Tree Species Fractions in Temperate Mixed Forests Using Sentinel-2 Time Series and Synthetically Mixed Training Data" (DOI: https://doi.org/10.1016/j.rse.2025.114740 )</p> <p>The dataset contains:</p> <ol> <li>A geopackage of training points of pure tree species</li> <li>The resulting 12-band tree species fraction map of Rhineland-Palatinate</li> <li>HSV-colored map of dominant tree species. For information which tree species are represented by the different colors, refer to the Supplemental in the original paper.</li> <li>CSV-table of predicted and reference propotion of the tree species in the validation polygon (the original polygon data can not be published due to data privacy regulations) </li> </ol> <p> </p>
Growth and metabolome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources
<p><em>Raw data: Metabolome of S. uvarum (Su) and S. cerevisiae (Sc) in wine fermentations in 13 different nitrogen conditions. Concentrations of compounds expressed in mg/L at 60 g/L CO2 sampling point and at the end of fermentation. The volatile compounds are grouped according to the chemical functional group (ethyl esters, acetate esters, higher alcohols, medium chain fatty acids (MCFA) and branched-chain fatty acids (BCFA)). The central carbon metabolites (CCM) and sugars conform the last group. Each value is the mean of three biological replicates. The raw data is reported in a processed form in the manuscript entitled “The growth and metabolome of Saccharomyces uvarum in wine fermentations is strongly influenced by the route of nitrogen assimilation” </em></p>
Transcriptome data of Saccharomyces uvarum grown in synthetic wine must with different nitrogen sources Created Jun 9, 2022 12:06:22 PM, modified Jun 9, 2022 12:07:57 PM
<p>Transcriptome analysis of S. uvarum grown on synthetic wine must with different nitrogen sources: Ammonium, Phenylalanine, Asparagine, or Methionine. This is a dataset for the thesis of Angela Coral (Autumn 2022) , which can be accessed at www.ucc.ie. The work will also be submitted for publication and this will be a supplementary data file.</p>
Synthetic multispectral & multitime data
<p>The synthetic image dataset 'mixed_4obj_time.tif' comprises four colour channels and 60 timepoints.</p> <p>It can be used to demonstrate spectral mixing/unmixing over time.</p>
Synthetic and field data to test RASE performance
<p>Texts 1 and 3 are synthetic data and labels; Texts 2 and 4 are field data and labels.<br> The data dimension is N*8640, where N is the number of collection days, and 8640 is the number of data sampling points.</p>
Synthetic Indoor Climate and Occupancy Data from Office and Meeting Room Simulations
<p>This is the dataset used for the publication "Coddora: CO2-based Occupancy Detection model<br>trained via DOmain RAndomization". The goal is to provide training data for occupancy detection.<br><br>The dataset contains one million days of data including 10 occupied days for each of 100,000 randomized room models (50,000 rooms considering office activity and 50,000 meeting room activity). Data were generated in EnergyPlus simulations according to the methodology described in the paper.<br><br>When using the dataset, please cite:</p> <blockquote> <p><em>Manuel Weber, Farzan Banihashemi, Davor Stjelja, Peter Mandl, Ruben Mayer, and Hans-Arno Jacobsen. 2024. Coddora: CO2-Based Occupancy Detection Model Trained via Domain Randomization. In International Joint Conference on Neural Networks (IJCNN). June 30 - July 5, 2024, Yokohama, Japan.</em></p> </blockquote> <h2>Dataset Structure</h2> <p>The following files are provided:<br><br> 1. dataset_office_rooms.h5 (provided as zip file)<br> 2. dataset_meeting_rooms.h5 (provided as zip file)<br> 3. simulated_occupancy_office_rooms.csv<br> 4. simulated_occupancy_meeting_rooms.csv</p> <p>Please use an archiving tool such as 7zip to unzip the hdf5 files.<br>Both hdf5 files contain two datasets with the following keys:<br><br> 1. "<em>data</em>": contains the simulated indoor climate and occupancy data<br> 2. "metadata": contains the metadata that were used for each simulation</p> <p>The csv files contain the time series of occupancy that were used for the simulations.<br><br></p> <h2>Data</h2> <p><em>Data</em> includes the following fields:</p> <p><em>Datetime:</em> day of the year (may be relevant due to seasonal differences) and time of the day<br><em>Zone Air CO2 Concentration:</em> CO2 level in ppm<br><em>Zone Mean Air Temperature:</em> temperature in °C<br><em>Zone Air Relative Humidity: </em>relative humidity in %<br><em>Occupancy: </em>level of occupancy relative to the maximum capacity of the room (in the range [0-1])<br><em>Ventilation:</em> fraction of window opening in the range [0.01, 1]<br><em>SimID:</em> foreign key to reference the room properties the simulation was based on<br><em>BinaryOccupancy:</em> 0 or 1 denoting absence or presence (for binary classification)</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th><em>Datetime</em></th> <th><em>Zone Air CO2 Concentration</em></th> <th><em>Zone Mean Air Temperature</em></th> <th><em>Zone Air Relative Humidity</em></th> <th><em>Occupancy</em></th> <th><em>Ventilation</em></th> <th><em>simID</em></th> <th><em>BinaryOccupancy</em></th> </tr> <tr> <td> <p>10/09 11:21:00</p> </td> <td> <p>1084.5624647371608</p> </td> <td> <p>24.545635909907148</p> </td> <td> <p>41.18393114737054</p> </td> <td> <p>0.7</p> </td> <td> <p>0.0</p> </td> <td>99</td> <td>1</td> </tr> </tbody> </table> <pre> </pre> <h2>Metadata</h2> <p><em>Metadata</em> includes the following fields. <br>Underscores denote that the field was not selected during randomization but calculated from the other values.</p> <p>width: room width in m<br>length: room length in m<br>height: hoom height in m<br>infiltration: infiltration per exterior area in m³/m²s<br>outdoor_co2: co2 concentration in the outdoor air in ppm (set to a random value between [300, 500])<br>orientation: angle between the room's facade orientation and the north direction in degrees<br>maxOccupants: room occupation limit, i.e. the maximum number of occupants<br>_floorArea: floor area in m² (calculated from room dimensions)<br>_volume: room volume in m³ (calculated from room dimensions)<br>_exteriorSurfaceArea: surface area of the facade wall (calculated from room dimensions)<br>_winToFloorRatio: ratio between total window area and floor area (calculated from room model)<br>firstDayUsedOfOccupancySequence: selected starting day in the sequence of occupancy data for rooms with the respective maxOccupants value<br>simID: unique identifier of the simulation to relate between simulation metadata and resulting simulated data</p> <p> </p> <p>Example row:</p> <table> <tbody> <tr> <th>width</th> <th>length</th> <th>height</th> <th>infiltration</th> <th>outdoor_co2</th> <th>orientation</th> <th>maxOccupants</th> <th>_floorArea</th> <th>_volume</th> <th>_exteriorSurfaceArea</th> <th>_winToFloorRatio</th> <th>firstDayOfUsedOccupancySequence</th> <th>simID</th> </tr> <tr> <td>5.481</td> <td>5.190</td> <td>3.264</td> <td>0.000214</td> <td>438.0</td> <td>316.0</td> <td>4.0</td> <td>28.446</td> <td>92.849</td> <td>16.940</td> <td>0.216</td> <td>192</td> <td>0</td> </tr> </tbody> </table> <p> </p> <h2>Occupancy Data</h2> <p>The occupancy data provided through the separate csv files contain the data from the upfront occupancy simulations that the climate simulation was based on. For each level of considered room occupancy limit (maxOccupants), the datasets provide minute values of occupancy throughout 1000 days.</p> <p><em>Datetime, </em><em>Date, </em><em>Timestamp: fictive time of simulated occupancy record (sequences are in 1-minute resolution)</em><br><em>Occupants: number of present occupants</em><br><em>Occupancy: binary occupancy state (0=unoccupied, 1=occupied)</em><br><em>WindowState: binary state of ventilation (0=windows closed, 1=room is ventilated)</em><br><em>maxOccupants: maximum number of occupants considered for the simulated sequence</em><br><em>WindowOpeningFraction: fractional extent to which windows are opened, within the interval [0.01, 1]<br><br></em></p> <p>Example row:</p> <table> <tbody> <tr> <th>Datetime</th> <th>Date</th> <th>Timestamp</th> <th>Occupants</th> <th>Occupancy</th> <th>WindowState</th> <th>maxOccupants</th> <th>WindowOpeningFraction</th> </tr> <tr> <td>2023-01-01 00:00:00</td> <td>2023-01-01</td> <td>1.672531e+09</td> <td>0</td> <td>0</td> <td>0</td> <td>1</td> <td>0.0</td> </tr> </tbody> </table> <p> </p> <p> </p>
Regional Moment Tensor Catalog (Declustered-Shallow Depth) for Northern Banda Arc Region-Indonesia (2009 to 2020) with Additional 3D Synthetic Data
<p>This dataset is produced using an innovative automated procedure that enhances the accuracy and reliability of moment tensor solutions, as described in Halauwet et al. (2024). The dataset includes RMT solutions for the period from 2009 to 2020 in the Northern Banda Arc Region. Additionally, synthetic data, test results and setup files used in the testing and validation of this procedure are included.<br><br>When using this data, please cite the following references:</p> <ul> <li>Halauwet, Y., Afnimar, Triyoso, W., Vackář, J., Daryono, Supendi, P., Daniarsyad, G., Simanjuntak, A. V. H., Pranata, B., Narwadan, H. A. A. M., & Hakim, M. L., Regional moment tensor catalog (declustered-shallow depth) for northern Banda Arc region-Indonesia (2009 to 2020) with additional 3D synthetic data [Data set]. <em>Zenodo</em>, 2024;, <a href="https://doi.org/10.5281/zenodo.10212539">https://doi.org/10.5281/zenodo.10212539</a></li> <li>Halauwet, Y., Afnimar, Triyoso, W., Vackář, J., Daryono, Supendi, P., Daniarsyad, G., Simanjuntak, A. V. H., Pranata, B., Narwadan, H. A. A. M., & Hakim, M. L., A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework, <em>Geophysical Journal International</em>, 2024;, ggae309, <a href="https://doi.org/10.1093/gji/ggae309">https://doi.org/10.1093/gji/ggae309</a></li> </ul> <p>Email: yehezkiel.halauwet@bmkg.go.id</p>
ENTICE Multi-objective optimization framework synthetic evaluation data-sets
<p>This dataset contains synthetic usage data for evaluation of the multi-objective redistribution framework for distributed VMI repositories. </p> <p>The data is stored in a Java object and it should be directly loaded. </p> <p> </p>
Synthetic Data for Neutrophil Analysis: Sets with irregular shapes and Poisson noise
<p><strong>Synthetic Datasets with irregular shapes and Poisson noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by <strong>irregular shapes </strong>(sum of Gaussians) and <strong>Poisson Noise</strong> (check the corresponding sets with regular shapes, i.e. Gaussians with Gaussian noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise) distributions of higher intensities than the background. The orientation of the Poisson varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Shapes were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Poisson noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 5):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData_P_mat_La </strong></li> <li><strong> First data set syntheticData_P1_mat_Re</strong></li> <li><strong> Second data set syntheticData_P2_mat_Re</strong></li> <li><strong> Third data set syntheticData_P3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData_P4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData_P5_mat_Re</strong></li> </ul> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></p>
Synthetic Data for Neutrophil Analysis: Sets with regular shapes and Gaussian noise
<p><strong>Synthetic Datasets with regular shapes and Gaussian noise.</strong></p> <p><strong>Part of the PhagoSight neutrophil tracking and analysis package (Henry, et al., PLOS ONE, 2013):</strong></p> <p> </p> <p>https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636</p> <p>http://www.phagosight.org</p> <p>https://github.com/phagosight/phagosight</p> <p> </p> <p>A series of synthetic data sets that reproduce different behaviour characteristics of migrating neutrophils were generated in MATLAB. The data sets consisted of six artificial neutrophils that travelled along paths that presented different conditions of tortuosity, times to activation and proximity to other neutrophils during 98 time frames.</p> <p>Numerous data sets of neutrophils in zebrafish were carefully observed before setting the characteristics. Six trajectories were manually determined by setting the row, column positions of the centroids at every time point for 98 time frames. Each trajectory was designed so that it would represent different neutrophil behaviours: some trajectories were very oriented and had movements with uniform distance between time frames, whilst others were less uniform and would move at different velocities, some were tortuous whilst others were straight. The trajectories of cells 1 and 2 collided several times in the second half of the time frames whilst cells 3 and 4 collided at the beginning of the movement. Cell 6 migrated without meandering and then stopped at the end (which represents the wound area of an inflammation-based experiment) whilst 5 presented a delayed activation. </p> <p>Each time frame consisted of 11 slices of z-stack each with 275 x 275 pixels, where the neutrophils were formed by Gaussian distributions of higher intensities than the background and <strong>Gaussian noise </strong>(check the corresponding irregular shapes with Poisson noise plus another set with a <strong>single large neutrophil</strong> and Poisson noise). The orientation of the Gaussians varied according to the displacement of the artificial neutrophils, <em>i.e.</em>they were round when the cells were static, or elongated when in movement. The tracks with the Gaussians were saved as the <em>gold standard</em> and five different data sets were generated by adding varying levels of white Gaussian noise resulting in data sets with distributions with increasing similarity between the neutrophils and the background reflected by the decreasing values of the Bhattacharyya Distance (1.61, 1.25, 1, 0.66, 0.45) as defined by Coleman 1979.</p> <p> </p> <p>Files corresponding to the sets with irregular shapes and Poisson noise (noise increases from 1 to 6):</p> <ul> <li><strong> x,y,t trajectories ThreeDTracks</strong></li> <li><strong> Ground Truth syntheticData0_mat_Re </strong></li> <li><strong> First data set syntheticData1_mat_Re</strong></li> <li><strong> Second data set syntheticData2_mat_Re</strong></li> <li><strong> Third data set syntheticData3_mat_Re</strong></li> <li><strong> Fourth data set syntheticData4_mat_Re</strong></li> <li><strong> Fifth data set syntheticData5_mat_Re</strong></li> <li><strong> Sixth data set syntheticData6_mat_Re</strong></li> </ul> <p> </p> <p>Corresponding GIF files are also included as illustrations of the cells in motion.</p> <p> </p> <p>Main Reference:</p> <p><a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072636"><strong><em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model</strong> </a><br> Henry KM, Pase L, Ramos-Lopez CF, Lieschke GJ, Renshaw SA, Reyes-Aldasoro CC. (2013) <em>PhagoSight</em>: An Open-Source MATLAB® Package for the Analysis of Fluorescent Neutrophil and Macrophage Migration in a Zebrafish Model. PLOS ONE 8(8): e72636. <a href="https://doi.org/10.1371/journal.pone.0072636">https://doi.org/10.1371/journal.pone.0072636</a></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.