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1,221 results for “Aggregators”
Risk factors modifying familial aggregation for lung cancer in affected individuals
<p>Base de Datos</p>
data on Marshall properties for asphalt mixtures containing recycled concrete aggregate
<p>these data set about Marshall stability and flow and density void analysis for asphalt mixture surface layer incorporating recycled as coarse aggregate. the data are collected during the work at the transportation lab at Baghdad University and the asphalt lab in Iben-Rushud government company.</p>
Database of physicochemical and optical properties of black carbon fractal aggregates
<p>In order to estimate the climate impact of highly absorbing black carbon (BC) aerosols, it is necessary to know their optical properties. The Lorentz-Mie theory, often used to calculate the optical properties of BC under the spherical morphological assumption, produces discrepancies when compared to measurements. In light of this, researchers are currently investigating the possibility of computing the optical properties of BC using a realistic fractal aggregate morphology. To determine the optical properties of such BC fractal aggregates, the Multiple Sphere T-Matrix method (MSTM) is used, which can take more than 24 hours for a single simulation depending on the aggregate properties. This study provides a highly accurate benchmark machine-learning algorithm that can be used to generate the optical properties of BC fractal aggregate in a fraction of a second. The machine learning algorithm was trained over an extensive database of physicochemical and optical properties of BC fractal aggregates. The extensive training data helped develop an ML algorithm that can accurately predict the optical properties of BC fractal aggregates with an average deviation of less than one percent from their actual values. Specifically, the ML algorithm provides the option to generate the optical properties in the visible spectrum using either kernel ridge regression (KRR) or artificial neural networks (ANN) for a BC fractal aggregate of desired physicochemical properties like size, morphology, and organic coating. The dataset of physicochemical and optical properties of BC fractal aggregates are provided here. The developed ML algorithm for predicting the optical properties of BC fractal aggregates (https://github.com/jaikrishnap/Machine-learning-for-prediction-of-BCFAs) is highly useful for real-world applications due to its wide parameter range, high accuracy, and low computational cost.</p> <p><strong>Contents</strong></p> <ul> <li>database_optical_properties_black_carbon_fractal_aggregtates.csv, data file, comma-separated values</li> <li>database_header.txt, metadata, text</li> </ul> <p><strong>Citation for the database: </strong></p> <p>B., Romshoo, T., Müller, B., Patil, J., Michels, T., Kloft, M., and Pöhlker, M.: Database of physicochemical and optical properties of black<br> carbon fractal aggregates, Dataset, https://doi.org/10.5281/zenodo.7523058, 2023.</p>
Cell behaviors underlying Myxococcus xanthus aggregate dispersal
<p>The soil bacterium Myxococcus <em> xanthus</em> is a model organism with a set of diverse behaviors. These behaviors include the starvation-induced multicellular development program, in which cells move collectively to assemble multicellular aggregates. After initial aggregates have formed, some will disperse, with smaller aggregates having a higher chance of dispersal. Initial aggregation is driven by two changes in cell behavior: cells slow down inside of aggregates and bias their motion by reversing direction less frequently when moving towards aggregates. </p> <p>However, the cell behaviors that drive dispersal are unknown. Here we use fluorescent microscopy to quantify changes in cell behavior after initial aggregates have formed. We observe that after initial aggregate formation, cells adjust the bias in reversal timings by initiating reversals more rapidly when approaching unstable aggregates. Using agent-based modeling, we then show dispersal is predominantly generated by this change in bias, which is strong enough to overcome slowdown inside aggregates. Notably, the change in reversal bias is correlated with the nearest aggregate’s size, connecting cellular activity to previously observed correlations between aggregate size and fate. To determine if this connection is consistent across strains, we analyze a second M. <em>xanthus</em> strain with reduced levels of dispersal. We find that far fewer cells near smaller aggregates modified their bias. This implies that aggregate dispersal is under genetic control, providing a foundation for further investigations into the role it plays in the life cycle of M. <em> xanthus</em>.</p> <p> </p>
Supplemental data for characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks
<p>This is the supplemental data for the manuscript titled <em>Characterization of mixing in nanoparticle hetero-aggregates using convolutional neural networks</em> submitted to <em>Nano Select</em>.</p> <p><strong>Motivation:</strong></p> <p>Detection of nanoparticles and classification of the material type in scanning transmission electron microscopy (STEM) images can be a tedious task, if it has to be done manually. Therefore, a convolutional neural network is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. The present dataset contains the training data and some jupyter-notebooks that can be used after installation of the MMDetection toolbox (<a href="https://github.com/open-mmlab/mmdetection">https://github.com/open-mmlab/mmdetection</a>) to train the CNN. Details are provided in the manuscript submitted to Nano Select and in the comments of the jupyter-notebooks.</p> <p><strong>Authors and funding:</strong></p> <p>The present dataset was created by the authors. The work was funded by the Deutsche Forschungsgemeinschaft within the priority program SPP2289 under contract numbers RO2057/17-1 and MA3333/25-1.</p> <p><strong>Dataset description:</strong></p> <p>Four jupyter-notebooks are provided, which can be used for different tasks, according to their names. Details can be found within the comments and markdowns. These notebooks can be run after installation of MMDetection within the mmdetection folder.</p> <ul> <li><em>particle_detection_training.ipynb:</em> This notebook can be used for network training.</li> <li><em>particle_detection_evaluation.ipynb:</em> This notebook is for evaluation of a trained network with simulated test images.</li> <li><em>particle_detection_evaluation_experiment.ipynb:</em> This notebook is for evaluation of a trained network with experimental test images.</li> <li><em>particle_detection_measurement_experiment.ipynb:</em> This notebook is for application of a trained network to experimental data.</li> </ul> <p>In addition, a script titled <em>particle_detection_functions.py</em> is provided which contains functions required by the notebooks. Details can be found within the comments.</p> <p>The zip archive <em>training_data.zip</em> contains the training data. The subfolder <em>HAADF</em> contains the images (sorted as training, validation and test images), the subfolder <em>json </em>contains the annotation (sorted as training, validation and test images). Each file within the <em>json</em> folder provides for each image the following information:</p> <ul> <li>aggregat_no: image id, the number of the corresponding image file</li> <li>particle_position_x: list of particle position x-coordinates in nm</li> <li>particle_position_y: list of particle position y-coordinates in nm</li> <li>particle_position_z: list of particle position z-coordinates in nm</li> <li>particle_radius: list of volume equivalent particle radii in nm</li> <li>particle_type: list of material types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></li> <li>particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</li> <li>rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</li> <li>deformation: list of particle deformations. After the first rotation the particle x-coordinates of the particle’s surface mesh are scaled by the factor listed in deformation, y- and z-coordinates are scaled according to 1/sqrt(deformation).</li> <li>cluster_index: list of cluster indices for each particle</li> <li>initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</li> <li>fractal_dimension: the intended fractal dimension of the aggregate</li> <li>fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</li> <li>fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</li> <li>fractal_prefactor: fractal prefactor</li> <li>mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</li> <li>mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</li> <li>mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</li> <li>particle_1_rho: density of TiO<sub>2</sub> used for the calculations</li> <li>particle_1_size_mean: mean TiO<sub>2</sub> radius</li> <li>particle_1_size_min: smallest TiO<sub>2</sub> radius</li> <li>particle_1_size_max: largest TiO<sub>2</sub> radius</li> <li>particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</li> <li>particle_1_clustersize: average TiO<sub>2</sub> cluster size</li> <li>particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</li> <li>particle_2_rho: density of WO<sub>3 </sub>used for the calculations</li> <li>particle_2_size_mean: mean WO<sub>3</sub> radius</li> <li>particle_2_size_min: smallest WO<sub>3</sub> radius</li> <li>particle_2_size_max: largest WO<sub>3</sub> radius</li> <li>particle_2_size_std: standard deviation of WO<sub>3</sub> radii</li> <li>particle_2_clustersize: average WO<sub>3</sub> cluster size</li> <li>particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</li> <li>particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</li> <li>number_of_primary_particles: number of particles within the aggregate</li> <li>gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</li> <li>gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</li> <li>mean_coordination: mean total coordination number (particle contacts)</li> <li>mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</li> <li>mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</li> <li>radius_equiv: list of area equivalent particle radii (in projection)</li> <li>k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</li> <li>polygons: list of polygons that surround the particle (COCO annotation)</li> <li>bboxes: list of particle bounding boxes</li> <li>aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</li> <li>n_pix: number of pixel per image in horizontal and vertical direction (squared images)</li> <li>pixel_size: pixel size in nm</li> <li>image_size: image size in nm</li> <li>add_poisson_noise: 1 if poisson noise was added, 0 otherwise</li> <li>frame_time: simulated frame time (required for poisson noise)</li> <li>dwell_time: dwell time per pixel (required for poisson noise)</li> <li>beam_current: beam current (required for poisson noise)</li> <li>electrons_per_pixel: number of electrons per pixel</li> <li>dose: electron dose in electrons per Å<sup>2</sup></li> <li>add_scan_noise: 1 if scan noise was added, 0 otherwise</li> <li>beam misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</li> <li>scan_noise: parameter that describes how far the beam can be misplaced in pixel (required for scan noise)</li> <li>add_focus_dependence: 1 if a focus effect is included, 0 otherwise</li> <li>data_format: data format of the images, e.g. uint8</li> </ul> <p>There are 24000 training images, 5500 validation images, 5500 test images, and their corresponding annotations. Aggregates and STEM images were obtained with the algorithm explained in the main work. The important data for CNN training is extracted from the files of individual aggregates and concluded in the subfolder <em>COCO</em>. For training, validation and test data there is a file <em>annotation_COCO.json</em> that includes all information required for the CNN training.</p> <p>The zip archive <em>experiment_test_data.zip</em> includes manually annotated experimental images. All experimental images were filtered as explained in the main work. The subfolder <em>HAADF</em> includes thirteen images. The subfolder <em>json</em> includes an annotation file for each image in COCO format. A single file concluding all annotations is stored in <em>json/COCO/annotation_COCO.json</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental images investigated in the manuscript. It contains four subfolders corresponding to the four investigated samples. All experimental images were filtered as explained in the manuscript.</p> <p>The zip archive <em>particle_detection.zip</em> includes the network, that was trained, evaluated and used for the investigation in the manuscript. The network weights are stored in the file <em>particle_detection/logs/fit/20230622-222721/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook files. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>particle_detection/logs/fit/20230622-222721/config_file.py</em>.</p> <p>There is no confidential data in this dataset. It is neither offensive, nor insulting or threatening.</p> <p>The dataset was generated to discriminate between TiO<sub>2 </sub>and WO<sub>3</sub> nanoparticles in STEM-images. It might be possible that it can discriminate between different materials if the STEM contrast is similar to the contrast of TiO<sub>2 </sub>and WO<sub>3</sub> but there is no guarantee.</p>
Mineral Protection rather than Aggregate Stability Enhanced Soil Organic Carbon Along an Elevated Gradient in Alpine Areas of Southwest China
<p>This data contains Background, Dominant plant and their biomass, Environmental variables, Aggregate stability, Fe/Al oxides, Mass of soil density fractions, Carbon contetn in each density fraction, Mass of aggregates, Carbon content in each aggregate class size, Ratio of carbon content in each soil density and aggregate fractions and Enzyme avtivity of our investigated sites. Total 46 factors were given.</p>
Monthly aggregated GLASS FAPAR V6 (250 m): 50th percentile monthly time-series (2005)
<p><strong>List of Subdatasets:</strong></p> <ul> <li>Long-term data: <a href="https://doi.org/10.5281/zenodo.8381409">2000-2021</a></li> <li>5th percentile (p05) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408654">2000</a>, <a href="https://doi.org/10.5281/zenodo.8411611">2001</a>, <a href="https://doi.org/10.5281/zenodo.8412712">2002</a>, <a href="https://doi.org/10.5281/zenodo.8413021">2003</a>, <a href="https://doi.org/10.5281/zenodo.8413689">2004</a>, <a href="https://doi.org/10.5281/zenodo.8414639">2005</a>, <a href="https://doi.org/10.5281/zenodo.8411609">2006</a>, <a href="https://doi.org/10.5281/zenodo.8414085">2007</a>, <a href="https://doi.org/10.5281/zenodo.8414960">2008</a>, <a href="https://doi.org/10.5281/zenodo.8415476">2009</a>, <a href="https://doi.org/10.5281/zenodo.8415686">2010</a>, <a href="https://doi.org/10.5281/zenodo.8412154">2011</a>, <a href="https://doi.org/10.5281/zenodo.8414082">2012</a>, <a href="https://doi.org/10.5281/zenodo.8411364">2013</a>, <a href="https://doi.org/10.5281/zenodo.8414933">2014</a>, <a href="https://doi.org/10.5281/zenodo.8415414">2015</a>, <a href="https://doi.org/10.5281/zenodo.8412246">2016</a>, <a href="https://doi.org/10.5281/zenodo.8414083">2017</a>, <a href="https://doi.org/10.5281/zenodo.8411366">2018</a>, <a href="https://doi.org/10.5281/zenodo.8415203">2019</a>, <a href="https://doi.org/10.5281/zenodo.8415549">2020</a>, <a href="https://doi.org/10.5281/zenodo.8387608">2021</a></li> <li>50th percentile (p50) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408710">2000</a>, <a href="https://doi.org/10.5281/zenodo.8408798">2001</a>, <a href="https://doi.org/10.5281/zenodo.8408866">2002</a>, <a href="https://doi.org/10.5281/zenodo.8415319">2003</a>, <a href="https://doi.org/10.5281/zenodo.8415619">2004</a>, <a href="https://doi.org/10.5281/zenodo.8415878">2005</a>, <a href="https://doi.org/10.5281/zenodo.8416080">2006</a>, <a href="https://doi.org/10.5281/zenodo.8416619">2007</a>, <a href="https://doi.org/10.5281/zenodo.8417164">2008</a>, <a href="https://doi.org/10.5281/zenodo.8417513">2009</a>, <a href="https://doi.org/10.5281/zenodo.8417708">2010</a>, <a href="https://doi.org/10.5281/zenodo.8415669">2011</a>, <a href="https://doi.org/10.5281/zenodo.8416000">2012</a>, <a href="https://doi.org/10.5281/zenodo.8416542">2013</a>, <a href="https://doi.org/10.5281/zenodo.8417055">2014</a>, <a href="https://doi.org/10.5281/zenodo.8417467">2015</a>, <a href="https://doi.org/10.5281/zenodo.8415747">2016</a>, <a href="https://doi.org/10.5281/zenodo.8416333">2017</a>, <a href="https://doi.org/10.5281/zenodo.8416835">2018</a>, <a href="https://doi.org/10.5281/zenodo.8417326">2019</a>, <a href="https://doi.org/10.5281/zenodo.8417589">2020</a>, <a href="https://doi.org/10.5281/zenodo.8388078">2021</a></li> <li>95th percentile (p95) monthly time-series: <a href="https://doi.org/10.5281/zenodo.8408949">2000</a>, <a href="https://doi.org/10.5281/zenodo.8409059">2001</a>, <a href="https://doi.org/10.5281/zenodo.8409154">2002</a>, <a href="https://doi.org/10.5281/zenodo.8409362">2003</a>, <a href="https://doi.org/10.5281/zenodo.8416487">2004</a>, <a href="https://doi.org/10.5281/zenodo.8417029">2005</a>, <a href="https://doi.org/10.5281/zenodo.8417833">2006</a>, <a href="https://doi.org/10.5281/zenodo.8417996">2007</a>, <a href="https://doi.org/10.5281/zenodo.8418308">2008</a>, <a href="https://doi.org/10.5281/zenodo.8418669">2009</a>, <a href="https://doi.org/10.5281/zenodo.8418986">2010</a>, <a href="https://doi.org/10.5281/zenodo.8417649">2011</a>, <a href="https://doi.org/10.5281/zenodo.8417816">2012</a>, <a href="https://doi.org/10.5281/zenodo.8417959">2013</a>, <a href="https://doi.org/10.5281/zenodo.8418253">2014</a>, <a href="https://doi.org/10.5281/zenodo.8418625">2015</a>, <a href="https://doi.org/10.5281/zenodo.8417759">2016</a>, <a href="https://doi.org/10.5281/zenodo.8417898">2017</a>, <a href="https://doi.org/10.5281/zenodo.8418076">2018</a>, <a href="https://doi.org/10.5281/zenodo.8418442">2019</a>, <a href="https://doi.org/10.5281/zenodo.8418751">2020</a>, <a href="https://doi.org/10.5281/zenodo.8392976">2021</a></li> </ul> <p><strong>General Description</strong></p> <p>The <i>monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</i> dataset is derived from <abbr title="glass.umd.edu/FAPAR/MODIS/250m/">250m 8d GLASS V6 FAPAR</abbr>. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes:</p> <ul> <li><strong>Long-term:</strong></li> </ul> <p>Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R<sup>2</sup> (p95.r2_m).</p> <ul> <li><strong>Monthly time-series:</strong></li> </ul> <p>Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month.</p> <p><strong>Data Details</strong></p> <ul> <li><strong>Time period:</strong> March 2000 – December 2021</li> <li><strong>Type of data:</strong> Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)</li> <li><strong>How the data was collected or derived:</strong> Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the <a href="https://github.com/scikit-map/scikit-map">Scikit-map</a> Python package.</li> <li><strong>Statistical methods used:</strong> for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95.</li> <li><strong>Limitations or exclusions in the data:</strong> The dataset does not include data for Antarctica.</li> <li><strong>Coordinate reference system:</strong> EPSG:4326</li> <li><strong>Bounding box (Xmin, Ymin, Xmax, Ymax):</strong> (-180.00000, -62.0008094, 179.9999424, 87.37000)</li> <li><strong>Spatial resolution:</strong> 1/480 d.d. = 0.00208333 (250m)</li> <li><strong>Image size:</strong> 172,800 x 71,698</li> <li><strong>File format:</strong> Cloud Optimized Geotiff (COG) format.</li> </ul> <p><strong>Support</strong></p> <p>If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: <a href="https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues">https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues</a></p> <p><strong>Reference</strong></p> <p>Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: <a href="https://doi.org/10.21203/rs.3.rs-3415685/v1">https://doi.org/10.21203/rs.3.rs-3415685/v1</a></p> <p><strong>Name convention</strong></p> <p>To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:</p> <ol> <li><strong>generic variable name:</strong> fapar = Fraction of Absorbed Photosynthetically Active Radiation</li> <li><strong>variable procedure combination:</strong> essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM)</li> <li><strong>Position in the probability distribution / variable type:</strong> p05/p50/p95 = 5th/50th/95th percentile</li> <li><strong>Spatial support:</strong> 250m</li> <li><strong>Depth reference:</strong> s = surface</li> <li><strong>Time reference begin time:</strong> 20000301 = 2000-03-01</li> <li><strong>Time reference end time:</strong> 20211231 = 2022-12-31</li> <li><strong>Bounding box:</strong> go = global (without Antarctica)</li> <li><strong>EPSG code:</strong> epsg.4326 = EPSG:4326</li> <li><strong>Version code:</strong> v20230628 = 2023-06-28 (creation date)</li> </ol>
Replication package for: "Information Aggregation Under Ambiguity: Theory and Experimental Evidence"
<p>The package contains the data and code to replicate all figures and tables in Galanis, Ioannou, and Kotronis (forthcoming), "Information Aggregation Under Ambiguity: Theory and Experimental Evidence", Review of Economic Studies.</p>
Raw data of "Aggregation of adult parasitic nematodes in sex-mixed groups analyzed by transient anomalous diffusion formalism."
<p>Manuscript abstract:</p> <p>Intestinal parasitic worms are widespread throughout the world, causing chronic infections in humans and animals. However, very little is known about the locomotion of the worms in the host gut. We studied the movement of <em>Heligmosomoides bakeri, </em>naturally infecting mice and used as animal model for roundworm infections. We investigated the locomotion of <em>H.bakeri</em> in simplified environments mimicking key physical features of the intestinal lumen, i.e. medium viscosity and intestinal villi topography. We found that the motion sequence of these nematodes is non-periodic, but the migration could be described by transient anomalous diffusion. Aggregation as a result of biased, enhanced-diffusive locomotion of nematodes in sex-mixed groups was detected. This locomotion is probably stimulated by mating and reproduction, while single nematodes moved randomly (diffusive). Natural physical obstacles as high mucus-like viscosity or villi topography, slowed down but did not entirely prevent nematodes aggregation. Additionally, the mean displacement rate of nematodes in sex-mixed groups of 3.0·10<sup>-3</sup> mm/s in mucus-like medium is in good agreement with estimates of migration velocities of 10<sup>-4</sup> to 10<sup>-3</sup> mm/s in the gut. Our data indicate <em>H.bakeri</em> motion to be non-periodic and their migration random (diffusive-like), but triggerable by the presence of kin.</p> <p>These are our raw data as well as our Python source code of the analysis algorithm.</p>
The Platelet Aggregation After tiCagrelor Inhibition and FentanYl Trial (PACIFY)
ClinicalTrials.gov study NCT02683707. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Consumption of Oral Artificial Sweeteners on Platelet Aggregation and Polyol Excretion
ClinicalTrials.gov study NCT04731363. IPD Sharing: NO. Countries: 1. Publications: 4.
AggRegated System Of sensOrs and Multimedia Monitors: Technology for innovAtion and personalizaTion of rEhabilitation Care. (ROOMMATE)
ClinicalTrials.gov study NCT06728020. IPD Sharing: YES. Countries: 1. Publications: 1.
Calcium Hydroxide, Mineral Trioxide Aggregate and Biodentine (Trademark) as Direct Pulp Capping of Permanent Teeth
ClinicalTrials.gov study NCT02492841. IPD Sharing: NO. Countries: 1. Publications: 3.
Progression Rate of MSA Under EGCG Supplementation as Anti-Aggregation-Approach
ClinicalTrials.gov study NCT02008721. IPD Sharing: Not stated. Countries: 1. Publications: 19.
Risk Factors on the Long-Term Success of Mineral Trioxide Aggregate Pulpotomy
ClinicalTrials.gov study NCT07312539. IPD Sharing: NO. Countries: 1. Publications: 5.
Discovery and genetic characterization of single cohort adult colonies with male aggregations, and preliminary evidence for lekking in a Malagasy kite spider (Isoxya, Gasteracanthinae)
Open the record for dataset details and reuse information.
Interacting effects of surface water and temperature on wild and domestic large herbivore aggregations and contact rates
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Data from: The deubiquitinase USP5 prevents accumulation of protein aggregates in cardiomyocytes
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Data from: Convergence and variation in tree growth trends at the aggregate level
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Data from: Therapeutic treatment with OLX-07010 inhibited tau aggregation and ameliorated motor deficits in an aged mouse model of tauopathy
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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.