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1,221 results for “Aggregators”
Searchable Index of Metadata Aggregators
<p>Searchable Index of Metadata Aggregators is a database that stores general information of metadata aggregators. This database is accompanied with the “A WDS guide to Metadata Aggregators for Repository Managers”. The Searchable Index of Metadata Aggregators is an up-to-date catalogue of Dataset Metadata Aggregators (DMAs), implemented as an access database. It was designed to fill in a gap found by the Harvestable Metadata Services Working Group (HMetS-WG) members of the World Data System’s International Technology Office (WDS-ITO). These include up-to-date resources giving an overview of current infrastructures used to syndicate dataset metadata. The database contains information on DMA's supported metadata standards and software interfaces, as well as documentation on how to be aggregated by each.</p> <p>The WDS Guide to Metadata Aggregators is a guidance document for the associated Searchable Index of Metadata Aggregators. We have defined DMAs as federated service infrastructures that foster the findability and accessibility of data products by enabling access to multiple, distributed metadata records via a single search interface. This guide gives a description of this catalogue and general guidance on how to use it. In the sections that follow, we give a short background to the Harvestable Metadata Services-Working Group project. Then, we outline the project's research methodology and the properties of the searchable index. Finally, we discuss this project's limitations, as well as its future development. Providing metadata to aggregators can significantly improve the findability of research data products.</p> <p>Together, this guidance document and dataset package are designed to provide research data repository managers with options for participation in federated research data systems, and support institutional repositories' harvestable metadata service implementation strategies. In addition, as developers in the global research data management community seek to create pathways and workflows across data, software and compute resources, we anticipate that they're likely to prioritize connecting sites, organizations and services that have already done a lot of work harmonizing content from disparate providers. In this context, this resource will be helpful for creating roadmaps and implementation plans for integration across science clouds.</p>
Data used in JAMES paper "Sensitivity of the Horizontal Scale of Convective Self‐Aggregation to Sea Surface Temperature in Radiative Convective Equilibrium Experiments Using a Global Nonhydrostatic Model"
<p>Data used in JAMES paper "Sensitivity of the Horizontal Scale of Convective Self‐Aggregation to Sea Surface Temperature in Radiative Convective Equilibrium Experiments Using a Global Nonhydrostatic Model" by Shuhei Matsugishi and Masaki Satoh doi: 10.1029/2021MS002636</p>
Theoretical and Experimental database for corannulene:water aggregates in a rare gas matrix. Structures and IR spectra.
<p>This database is linked to the article entitled "Water clusters in interaction with corannulene in a rare gas matrix: structures, stability and IR spectra" by Leboucher et al. submitted to "Photochem" on March 1st 2022.</p> <p>Theoretical results can be found in the following directories:<br> - Geoms which contains the DFTB/FF optimized structures reported in Figures 2 to 4 of the manuscript (the last column of the files must not be taken into account)<br> - IR_harm which contains the IR harmonic data for these structures (first column: wavenumbers in cm-1, second column: intensities in km/mol)<br> - Spect_10K which contains the dynamics spectra as reported in Figures 5 to 7</p> <p>Experimental results can be found in the directory Experimental, with spectra in two columns (first column: wavenumber in cm-1, second column: absorbance). Data have been corrected for atmospheric water vapor.</p> <p>In the directory Gas-phase are reported the results of gas-phase calculations at the DFT (M062X/d95v(dip) and DFTB levels performed for benchmark purpose.<br> - in DFT_opt are reported DFT optimized structures, similar to DFTB optimized structures<br> - in IR-spect, harmonic DFT and DFTB spectra<br> - the En_GP.pdf file reports energetic data for these systems.</p> <p> </p>
Street Spectra aggregated classifications
CSV file containing aggregated classifications for light sources data and metadata.
Data and code for Host and pathogen drivers of infection-induced changes in social aggregation behavior
<p>Raw data and R code </p> <p>DistanceInds.xlsx contains pairwise distances between pairs of flies, measured within groups of 12 every 30 mins for 4 hours post-infection with one of four bacterial pathogens, at either a low or high dose. </p> <p>NND-Boyle.csv contains nearest-neighbor distances between pairs of flies, measured within groups of 12 following infections with Pseudomonas entomophila. </p>
Data for "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks"
<p>These are the data associated with the paper, "Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks" (DOI 10.1039/D2SM00452F). Three of the directories contains subdirectories with `GSD` files dumped from HOOMD. The other contains pretrained RNN models as TorchScript binaries exported from PyTorch.</p>
Dispersal, kin aggregation, and the fitness consequences of not spreading sibling larvae
<p>GENERAL INFORMATION</p> <p>1. Title of Dataset: Dispersal, kin aggregation, and the fitness consequences of not spreading sibling larvae</p> <p>2. Author Information<br> A. Principal Investigator Contact Information<br> Name: Scott Burgess<br> Institution: Florida State University<br> Address: 319 Stadium Drive, Tallahassee, FL, USA 32306<br> Email: sburgess@bio.fsu.edu</p> <p><br> 3. Date of data collection (single date, range, approximate date): 2016-2017</p> <p>4. Geographic location of data collection: Turkey Point, Florida, USA</p> <p>5. Information about funding sources that supported the collection of the data: National Science Foundation (NSF; OCE-1948788) and the Florida State University Council on Research and Creativity</p> <p> </p> <p>DATA & FILE OVERVIEW</p> <p>1. File List:<br> Figure 2.R<br> Figure 3.R<br> Figure 4.R<br> Figure 5.R<br> Observed sibship.R<br> Clark Evans Corrected function.R</p> <p>Figure 2 data.csv<br> Figure 3 data.csv<br> Figure 4 data.csv<br> Figure 5 data.csv<br> sampleIDfile_msats.csv<br> Sibships.csv</p> <p><br> 2. Relationship between files:<br> Figure 2.R uses Figure 2 data.csv<br> Figure 3.R uses Figure 3 data.csv and Clark Evans Corrected function.R<br> Figure 4.R uses Figure 4 data.csv and Clark Evans Corrected function.R<br> Figure 5.R uses Figure 5 data.csv<br> Observed sibship.R uses sampleIDfile_msats.csv and Sibships.csv</p> <p> </p> <p>3. Metadata</p> <p>Figure 2 data.csv<br> deployment: Sequential number for each deployment date<br> deployment.date: The date on which settlement plates were attached to poles in the field<br> treatment: Control = No colony in the center of the array; Treatment = Seven colonies placed in the center of the array.<br> sheet: Unique identifier for each settlement plate (=sheet)<br> distance.m: Distance, in meters, of the settlement plate to the center of the array<br> settlers: The number of settlers recorded in each settlement plate after retrieval<br> days: The number of days between the deployment and retrieval of settlement plates<br> settlers.day: settlers / days = the number of settlers per day</p> <p> </p> <p><br> Figure 3 data.csv<br> D.Date: Date on which settlement plates were Deployed<br> R.Date: Date on which settlement plates were Retrieved<br> D.Group: Sequential number for each deployment group<br> Plate.ID: Unique identifier for each settlement plate<br> Deployment: 1 = settlement plates deployed for 3 days; 2 = settlement plates placed back into the water after 3 days, and collected again after another 3 days (capturing larvae that settled on between day 4 and 6).<br> Point: Unique identifier for each settler<br> Raw.X: The distance, in millimeters, from the left side of the image<br> Raw.Y: The distance, in millimeters, from the bottom side of the image<br> True.X: The distance, in millimeters, from the left side of the focal settlement area<br> True.Y: The distance, in millimeters, from the bottom side of the focal settlement area</p> <p> </p> <p>Figure 4 data.csv<br> Sheet ID: Unique identifier for each settlement plate (=sheet)<br> Relatedness: Sib = each settler came from the same mother (half-sib or full-sib); NonSibs = each settler came from a different (unrelated) mother.<br> Point: Unique identifier for each settler<br> Raw.X: The distance, in millimeters, from the left side of the image<br> Raw.Y: The distance, in millimeters, from the bottom side of the image<br> True.X: The distance, in millimeters, from the left side of the focal settlement area<br> True.Y: The distance, in millimeters, from the bottom side of the focal settlement area</p> <p> </p> <p>Figure 5 data.csv<br> mother.ID: Unique identifier for each maternal colony<br> sib.total: A code representing the concatenation of sib.group.size and total.group.size<br> sib.group.size: The total number of focal siblings in the group<br> total.group.size: The total number of all settlers in the group<br> age.d: Days since settlement<br> Ind: F = focal individual (an offspring from the corresponding mother.id); NF = Non-focal individual (comes from another maternal colony, NOT from the corresponding mother.id)<br> sheet: Unique identifier for each settlement plate (=sheet)<br> survival: 1 = alive; 0 = dead.<br> bifurcations: The number of bifurcations on the colony.</p> <p>sampleIDfile_msats.csv<br> Ind: Individual ID for each colony that also indicates site ("ML_"). ML=Marine Lab.<br> Sample: Individual ID for each colony.<br> Group: A unique code indicating a seagrass blade. I.e., samples from Group 1 were from the seagrass blade #1, and so on.</p> <p>Sibships.csv<br> OffspringID1: Individual ID for each colony that matches 'Sample' in sampleIDfile_msats.csv.<br> OffspringID2: Individual ID for each colony that matches 'Sample' in sampleIDfile_msats.csv.<br> Probability: Probability (estimated from COLONY) that OffspringID1 and OffspringID2 are sibship type listed under SibshipType<br> Run: Run number from COLONY<br> Sibship Type: Whether COLONY estimated OffspringID1 and OffspringID2 to have a Full of Half sib relationship.</p>
Aggregated Network and Application Twitch.tv Live Streaming Dataset
<p>This dataset contains application and network measurements of 6,982 individual Twitch.tv streaming sessions of 222 different streamers summing up to more than 1,000h live streaming. The data are aggregated to uplink requests.</p>
Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"
<p>Datasets corresponding to "Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates"</p> <p> </p> <p>Please find below an explanation for the <strong>files </strong>in this repository:</p> <p><br> <br> <strong>DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z</strong></p> <p>Experimental data. To reproduce the analyses, unzip both files and put the content into a folder called "Dataset"</p> <p><strong>02_CNN_PhenotypeClassif.7z</strong></p> <p>CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder.</p> <p><strong>03_ExampleMeasurement.zip</strong></p> <p>One measurement file and a corresponding scatterplot</p> <p><strong>04_Dataset_load.zip</strong></p> <p>The python script "03_ExtractFeatures.py" loads the list of available experiment files (01_Dataset_Table_v02.csv). The experiment files are contained in DiseaseClassifPaper_Dataset_01.7z, DiseaseClassifPaper_Dataset_02.7z. The scrip then evaluates each experiment file to obtain distribution parameters for Area and Solidity. These values are written to new "01_Dataset_Table_v03.csv".</p> <p><strong>05_RF_training</strong></p> <p>Scripts to train and evaluate the Random Forest model (using features contained in "01_Dataset_Table_v03.csv").</p> <p><strong>07_pytranskit</strong></p> <p>Scripts for training and evaluating CDT-PLDA classifier</p> <p> </p> <p> </p>
Monovalent ion-mediated charge-charge interactions drive aggregation of surface-functionalized gold nanoparticles
<p>Dataset containing files required to run the simulations in "Monovalent ion-mediated charge-charge interactions drive aggregation of surface-functionalized gold nanoparticles"</p>
Alpha-synuclein aggregates are phosphatase resistant
<p>Data and uncut images from manuscript.</p>
A Global Gridded Municipal Water Withdrawal Estimation Method Using Aggregated Data and Artificial Neural Network
<p>Global gridded municipal water withdrawal estimations for the following WST paper.</p> <p>Jiabao Yan, Shaofeng Jia; A global gridded municipal water withdrawal estimation method using aggregated data and artificial neural network. <em>Water Science Technology</em>, 2023; 87 (1): 251–274. <a href="https://doi.org/10.2166/wst.2022.399" target="_blank" rel="noopener">https://doi.org/10.2166/wst.2022.399</a></p> <p>The representative year of the data is 2015, and the unit of the data is in millimeters (mm).</p>
Replication package for: Search Complementarities, Aggregate Fluctuations, and Fiscal Policy
<p>Fernández-Villaverde J, Mandelman F, Yu Y, Zanetti F. Search complementarities, aggregate fluctuations, and fiscal policy. <em>Review of Economic Studies</em></p>
A consistent dataset for net income deciles for 190 countries, aggregated to 32 geographical regions from 1958-2015
<p>This is a data record which corresponds to the paper "A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015" (Narayan et al. 2024, ESSD)</p> <p>The final paper is available here-https://essd.copernicus.org/articles/16/2333/2024/</p> <p>Description/Abstract-Data on income distributions within and across countries are becoming increasingly important for informing analysis of income inequality and understanding the distributional consequences of climate change. While datasets on income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same concept of inequality (or income concept) and therefore make comparisons across countries, over time and across datasets difficult. Here, we present a consistent dataset of income distributions across 190 countries from 1958 to 2015 measured in terms of net income. We complement the observed values in this dataset with values imputed from a summary measure of the income distribution, specifically the Gini coefficient. For the imputation, we use a recently developed nonparametric principal-component-based approach that shows an excellent fit to data on income distributions compared to other approaches. We also present another version of this dataset aggregated from the country level to 32 geographical regions. Our dataset is developed for the purpose of calibrating models such as integrated human–Earth system models with detailed data on income distributions. This dataset will enable more robust analysis of income distribution at multiple scales. </p> <p>Citation for paper- Narayan, K. B., O'Neill, B. C., Waldhoff, S., and Tebaldi, C.: A consistent dataset for the net income distribution for 190 countries and aggregated to 32 geographical regions from 1958 to 2015, Earth Syst. Sci. Data, 16, 2333–2349, https://doi.org/10.5194/essd-16-2333-2024, 2024.</p> <p> </p>
Supplemental material for 'Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection'
<p>This is the supplemental data for the manuscript titled ‘<em>Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection’</em> submitted to <em>Ultramicroscopy</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 (CNN) is trained to do this task for STEM-images of TiO<sub>2</sub>-WO<sub>3</sub> nanoparticle hetero-aggregates. In conventional STEM, only 2D-projection images of the samples can be measured. STEM tomography allows for a 3D-reconstruction but it is a time-consuming and hence expensive task. In the present work, evaluations of 2D-projections are compared quantitatively to evaluations of 3D-reconstructions. For both evaluations a CNN is trained to predict particle positions and classify the material. The present dataset contains training and evaluation data and some code scripts 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 for 2D-projection data. For 3D-reconstruction a StarDist-3D network is trained (<a href="https://github.com/stardist/stardist">https://github.com/stardist/stardist</a>). Details are provided in the manuscript submitted to Ultramicroscopy and in the comments of the code scripts. For evaluation, we provide Python and MATLAB scripts.</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 and under contract number INST 144/462-1 FUGG.</p> <pre> </pre> <p><strong>Dataset description:</strong></p> <p>We provide several zip-archives. All of them contain two subfolders, one of them for 2D-projection data, the other one for 3D-reconstruction data.</p> <p><em>training_data.zip</em> contains the training data. In the 3D case, the subfolder <em>mask</em> contains the ground truth segmentation masks; the subfolder <em>reconstruction</em> contains the corresponding simulated 3D reconstructions. In the 2D case, the subfolder <em>HAADF</em> contains the 2D-projection images. In both cases, the subfolder <em>json </em>contains the annotation. Each file within the <em>json</em> folder provides for each image or reconstruction the following information:</p> <p>· aggregat_no: image id, the number of the corresponding image file</p> <p>· particle_position_x: list of particle position x-coordinates in nm</p> <p>· particle_position_y: list of particle position y-coordinates in nm</p> <p>· particle_position_z: list of particle position z-coordinates in nm</p> <p>· particle_radius: list of volume equivalent particle radii in nm</p> <p>· particle_type: list of particle types, 1: TiO<sub>2</sub>, 2: WO<sub>3</sub></p> <p>· particle_shape: list of particle shapes: 0: sphere, 1: box, 2: icosahedron</p> <p>· rotation: list of particle rotations in rad. Each particle is rotated twice by the listed angle (before and after deformation)</p> <p>· 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).</p> <p>· cluster_index: list of cluster indices for each particle</p> <p>· initial_cluster_index: list of initial cluster indices for each particle, before primary clusters of the same material were merged</p> <p>· fractal_dimension: the intended fractal dimension of the aggregate</p> <p>· fractal_dimension_true: the realized geometric fractal dimension of the aggregate (neglecting particle densities)</p> <p>· fractal_dimension_weight_true: the realized fractal dimension of the aggregate (including particle densities)</p> <p>· fractal_prefactor: fractal prefactor</p> <p>· mixing_ratio_intended: the intended mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_true: the realised mixing ratio (fraction of WO<sub>3</sub> particles)</p> <p>· mixing_ratio_volume: the realised mixing ratio (fraction of WO<sub>3</sub> volume)</p> <p>· mixing_ratio_weight: the realised mixing ratio (fraction of WO<sub>3</sub> weight)</p> <p>· particle_1_rho: density of TiO<sub>2</sub> used for the calculations</p> <p>· particle_1_size_mean: mean TiO<sub>2</sub> radius</p> <p>· particle_1_size_min: smallest TiO<sub>2</sub> radius</p> <p>· particle_1_size_max: largest TiO<sub>2</sub> radius</p> <p>· particle_1_size_std: standard deviation of TiO<sub>2</sub> radii</p> <p>· particle_1_clustersize: average TiO<sub>2</sub> cluster size</p> <p>· particle_1_clustersize_init: average TiO<sub>2</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_1_clustersize_init_intended: intended TiO<sub>2</sub> cluster size of primary clusters</p> <p>· particle_2_rho: density of WO<sub>3 </sub>used for the calculations</p> <p>· particle_2_size_mean: mean WO<sub>3</sub> radius</p> <p>· particle_2_size_min: smallest WO<sub>3</sub> radius</p> <p>· particle_2_size_max: largest WO<sub>3</sub> radius</p> <p>· particle_2_size_std: standard deviation of WO<sub>3</sub> radii</p> <p>· particle_2_clustersize: average WO<sub>3</sub> cluster size</p> <p>· particle_2_clustersize_init: average WO<sub>3</sub> cluster size of primary clusters (before merging into larger clusters)</p> <p>· particle_2_clustersize_init_intended: intended WO<sub>3</sub> cluster size of primary clusters</p> <p>· number_of_primary_particles: number of particles within the aggregate</p> <p>· gyration_radius_geometric: gyration radius of the aggregate (neglecting particle densities)</p> <p>· gyration_radius_weighted: gyration radius of the aggregate (including particle densities)</p> <p>· mean_coordination: mean total coordination number (particle contacts)</p> <p>· mean_coordination_heterogen: mean heterogeneous coordination number (contacts with particles of the different material)</p> <p>· mean_coordination_homogen: mean homogeneous coordination number (contacts with particles of the same material)</p> <p>· material_1: the name of the first material (TiO2)</p> <p>· material_2: the name of the second material (WO3)</p> <p>· radius_equiv: list of area equivalent particle radii (in projection) in nm</p> <p>· k_proj: projection direction of the aggregate: 0: z-direction (axis = 2), 1: x-direction (axis = 1), 2: y-direction (axis = 0)</p> <p>· polygons: list of polygons that surround the particle (COCO annotation)</p> <p>· bboxes: list of particle bounding boxes</p> <p>· aggregate_size: projected area of the aggregate translated into the radius of a circle in nm</p> <p>· n_pix: number of pixel per image in horizontal and vertical direction (squared images)</p> <p>· pixel_size: pixel size in nm</p> <p>· image_size: image size in nm</p> <p>· add_poisson_noise: 1 if poisson noise was added, 0 otherwise</p> <p>· frame_time: simulated frame time (required for poisson noise)</p> <p>· dwell_time: dwell time per pixel (required for poisson noise)</p> <p>· beam_current: beam current (required for poisson noise)</p> <p>· electrons_per_pixel: number of electrons per pixel</p> <p>· dose: electron dose in electrons per Å<sup>2</sup></p> <p>· add_scan_noise: 1 if scan noise was added, 0 otherwise</p> <p>· beam_misposition: parameter that describes how far the beam can be misplaced in pm (required for scan noise)</p> <p>· scan_noise: parameter that describes how far the beam can be misplaced in pix (required for scan noise)</p> <p>· add_focus_dependence: 1 if a focus effect is included, 0 otherwise</p> <p>· add_partial_coating: 1 if some TiO<sub>2</sub> particles were coated by a thin WO<sub>3</sub> film, 0 otherwise.</p> <p>· data_format: data format of the images, e.g. uint8</p> <p>For 3D reconstructions, the following information is added:</p> <p>· add_image_shifts, 1 if all images of the tilt series were shifted randomly by some pixel to account for misaligned images, 0 otherwise.</p> <p>· add_projection_noise: 1 if noise was added to projection angles, 0 otherwise.</p> <p>· N_SIRT: number of SIRT iterations for the 3D-reconstruction.</p> <p>· proj_angles: list of angles used for the projection directions of the tilt series in rad.</p> <p>For the 2D case, 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>For the 3D case, there are 100 simulated reconstructions that are divided into 80 training and 20 validation images within the training script.</p> <p>The zip archive <em>models.zip</em> includes the two networks that were trained, evaluated and used for the investigation in the manuscript. In the 2D case, network weights are stored in the file <em>2D_projection/logs/fit/20240209-095952/iter_60000.pth</em>. These weights can be loaded with the jupyter-notebook <em>2D_projection_prediction.ipynb</em>. Furthermore, a configuration file, which is required by the notebooks, is stored as <em>2D_projection/logs/fit</em> <em>20240209-095952/config_file.py</em>. In the 3D case, network weights are stored in <em>3D_reconstruction/model/weights_best.h5</em>. Also for this case, a configuration file is provided. The network can be loaded with the jupyter-notebook <em>3D_reconstruction_prediction.ipynb</em>.</p> <p>The zip archive <em>experiment_measurement.zip</em> includes the experimental 2D-projection images and the experimental 3D-reconstructions investigated in the manuscript. In the 3D case, we provide measured reconstructions as obtained by MATLAB and as transformed for the prediction with the CNN.</p> <p>The zip archive<em> experiment_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs.</p> <p>The zip archive <em>simulation_measurement.zip</em> includes simulated 2D-projection images and simulated 3D-reconstructions that were not used for the network training. These simulations were used for evaluation of trained networks.</p> <p>The zip archive<em> simulation_prediction.zip</em> includes predictions and visualizations obtained by the 2D and 3D CNNs for the simulated data that was not used during the training process.</p> <p>In the zip archive <em>code.zip</em>, we provide several files with Python and MATLAB code that can be used for training, prediction and evaluation of the CNNs. A lot of information is provided within the comments and markdowns. For the application, it is required that the MMDetection toolbox and the StarDist3D framework are installed.</p> <p>· <em>2D_projection_training.py:</em> This Python script can be used for network training of the 2D Mask R-CNN after installation of the MMDetection toolbox.</p> <p>· <em>3D_reconstruction_training.py</em>: This Python script can be used for network training of the StarDist-3D network after installation of the StarDist package.</p> <p>· <em>2D_projection_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained Mask R-CNN to experimental and simulated 2D-projection data.</p> <p>· <em>3D_reconstruction_prediction.ipynb</em>: This jupyter-notebook can be used for the application of a trained StarDist-3D network to experimental and simulated 3D-reconstruction data.</p> <p>· <em>Evaluation_experiment.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D experimental evaluations.</p> <p>· <em>Evaluation_simulation.m</em>: This MATLAB script is for the visualization and quantitative comparison of 2D and 3D evaluations of simulations.</p> <p>· <em>particle_detection_functions.py:</em> This Python script contains functions required by the jupyter-notebooks. Details can be found within the comments.</p> <p>· <em>ASTRA_CM_plot_results.m</em>: This MATLAB script provides functions for the visualization of experimental and simulated 3D-reconstructions.</p> <p>· <em>ASTRA_CM_particle_detection.m</em>: This MATLAB script is used for the quantitative evaluation of segmentations of 3D-reconstructions.</p> <p> </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 and STEM tomography reconstructions. 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>
Fig. 5 in New Species Of The Genus Pachyrhynchus Germar (Coleoptera, Curculionidae, Entiminae) From The Greater Mindanao Pleistocene Aggregate Island Complex (Philippines)
Fig. 5. Aedegal body of P. occidentalis; A – lateral view; B – frontal view. Scale 1mm.
Dataset: 2023 Aircraft traffic and GPS anomalies aggregated per hexbins
<p>We divided the globe into hexbins, each with an average area of 385 square kilometers. Once the hexbin grid was established, data from the GPS gaps, GPS deviations, and Traffic Density datasets were used to populate these hexbins with relevant information. On average, each hexbin has around 23,478 flights passing through it.</p> <ul> <li><strong>Total Records</strong>: 14,117 - total number of hexbin on a map, where number of flights > 0</li> <li><strong>Columns</strong>:</li> <ul> <li><strong>id:</strong></li> <li><strong>WKT</strong>: Well-Known Text representation of a POINT (senter of each hexbin) in the CSV file, or a geometry field in the DPKG file.</li> <li><strong>flights</strong>: number of flights traveled trough that hexbin in 2023</li> <li><strong>gaps</strong>: number of GPS gap incidents registered in that hexbin in 2023</li> <li><strong>deviations</strong>: number of GPS deviation incidents started in that hexbin in 2023</li> </ul> </ul>
Interacting effects of surface water and temperature on wild and domestic large herbivore aggregations and contact rates
<p>Earth's climate is rapidly changing, bringing forth questions of how domestic and wild animals will alter their behavior in response to increasing temperatures and dryland expansion. Dwindling water availability will likely impact animal behavior and water foraging, potentially increasing animal aggregations and interspecific contacts. These interspecific contacts are especially important for competition, predation, and disease transmission among wildlife and domestic animals.</p> <p>In this study, we analyzed interspecific wildlife and cattle contacts using two years of camera trap data at an experimental water manipulation site at a conservancy in central Kenya.</p> <p>We found that on average, the hourly probability of any interspecific contact was approximately 3.4 times higher at water sources versus drained water sources, and 18 times higher than surrounding matrix areas, and that this relationship was amplified by dry and hot conditions.</p> <p>Species-specific analyses revealed variation in the magnitude of responses across wildlife and domestic cattle, although all animals had approximately 2-3 times higher interspecific contact probability with other species at water in hot conditions versus other conditions. Notably, we observed the largest behavioral changes for relatively water-independent species, such as giraffe, which had 3.6 times higher interspecific contact probability at water sources in hot versus other conditions.</p> <p><em>Synthesis and applications:</em> These findings show how elevated temperatures that will become increasingly common with future climate changes can increase interspecific contacts around critical water resources. In mixed wildlife-livestock systems, maintaining wildlife-only water sources may be a practical management tool to mitigate human-wildlife conflict and disease transmission at this interface, especially during dry and hot conditions.</p>
Data for: Bimolecular Sandwich Aggregates of Porphyrin Nanorings
<p><em><span>Supporting data files for "Bimolecular Sandwich Aggregates of Porphyrin Nanorings”</span></em></p> <p><span><span>(a) <strong>PALES_structures</strong></span>. Files for analyzing RDCs: input text file (1 txt file), input geometries (61 pdb files) and output (61 txt files).</span></p> <p><span>(b) <strong>XTB_optimized_structures</strong></span>. Geometries of bimolecular aggregates of <strong><em>c-</em>P8</strong> and <strong><em>c-</em>P12</strong> with different substituents (4 xyz files).</p> <p><span><span>(c) <strong>TS_trajectory_structures</strong></span>. Calculated geometries along the transition state trajectory for intramolecular rotation of the <strong>(<em>c-</em>P12_t-Bu)2</strong> aggregate (9 xyz files).</span></p> <p><span><span>(d) <strong>Planarization_calculations</strong></span>. DFT calculated geometries of <strong><em>c-</em>P6</strong>, <strong><em>c-</em>P8</strong>, <strong><em>c-</em>P10</strong>, <strong><em>c-</em>P12</strong> and <strong><em>c-</em>P14</strong> when 2D planar (optimized in xy-plane) or 3D cylindrical (10 xyz files).</span></p> <p><span><span>(e) <strong>Molecular_Dynamics_GROMACS_input_files</strong></span>. Molecular dynamics input files for <strong>(<em>c-</em>P8_OOct)2</strong> and <strong>(<em>c-</em>P12_tBu)2</strong> (2x gro+top+itp+4xmdp).</span></p> <p><span><span>(f) <strong>cP12_tBu_2_structure</strong></span>. Idealized structure for the <strong>(<em>c-</em>P12_t-Bu)2</strong> aggregate as shown in Figure 3 of the manuscript (1 xyz file).</span></p>
Silene uralensis aggregate, circumpolar species : dataset, Miseq Illumina RAW READS
<p>This dataset includes 43 samples of circumpolar species included in the <em>Silene uralensis</em> aggregate, sensu http://panarcticflora.org/. Forty-eight low copy nuclear genes were enriched with <em>Silene-</em>specific probes. The samples were sequenced with the Miseq technology from the short read Illumina platform. An excel sheet with samples information is included.</p> <p>Two other datasets are associated to this one, called "Silene uralensis aggregate, circumpolar species : dataset, Novaseq Illumina RAW READS SET 1" on Zenodo 10.5281/zenodo.12699639 and "Silene uralensis aggregate, circumpolar species : dataset, Novaseq Illumina RAW READS SET 2" on Zenodo 10.5281/zenodo.12700012. These three datasets belong to a study about phylogenetics in the circumpolar <em>Silene uralensis</em> aggregate. </p> <div> <div> <div> </div> <div> <div> <div> </div> <div> <p> </p> <p> </p> </div> </div> </div> </div> </div>
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.