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8,565 results for “characterization”
Figure 1 in Identification and molecular characterization of Otobius megnini (Ixodida: Argasidae) seen in humans in Muş province, Turkey
Figure 1. PCR amplification of 16S rRNA gene region in Otobius megnini (Amplicon length 360 bp).
Figure 1 in Bats (Mammalia, Chiroptera) from Yuscarán in Eastern Honduras: Conservation and acoustic characterization for the insectivorous species
Figure 1. Geographic location of the study sites in the Yuscarán Biological Reserve and Municipality of Yuscarán, Department of El Paraíso, Honduras, Central America. Geographic coordinates and other details are in Table 1.
Figure 4 in Bats (Mammalia, Chiroptera) from Yuscarán in Eastern Honduras: Conservation and acoustic characterization for the insectivorous species
Figure 4. Echolocation pulses of aerial insectivorous bats. Spectrograms (bottom) and oscillograms (top) correspond to search calls. X axis milliseconds (ms) and Y axis Kilohertz (kHz). Emballonuridae: (BPl) B. plicata, (PMA) P. macrotis; Molossidae: (MAL) M. alvarezi, (MNI) M. nigricans, (MMO) M. molossus. Mormoopidae: (PFU) P. fulvus, (PGY) P. gymnonotus, (PME) P. mesoamericanus, (PPS) P. psilotis. Vespertilionidae: (NIG) M. nigricans, (BRA) E. brasiliensis, (FUR) E. furinalis, (FUS) E. fuscus.
Figure 3 in Bats (Mammalia, Chiroptera) from Yuscarán in Eastern Honduras: Conservation and acoustic characterization for the insectivorous species
Figure 3. Part of the bat species captured with mist nets, in the Yuscarán Biological Reserve and Municipality of Yuscarán, Department of El Paraíso, Honduras, Central America. (A) M. megalophylla; (B) D. rotundus; (C) L. aurita; (D) P. discolor; (E) A. geoffroyi; (F) G. leachii; (G) G. mutica; (H) C. perspicillata; (I) A. jamaicensis; (J) A. lituratus; (K) C. salvini; (L) D. azteca; (M) D. phaeotis; (N) S. hondurensis; (O) S. parvidens; (P) E. fuscus. Photos: (C) D.J.M.Q.; (A-P) W.N.G.C.
Figure 2 in Bats (Mammalia, Chiroptera) from Yuscarán in Eastern Honduras: Conservation and acoustic characterization for the insectivorous species
Figure 2. Species accumulation curve using mist nets, in the Yuscarán Biological Reserve and Municipality of Yuscarán, Department of El Paraíso, Honduras, Central America.
Figure 1 in Characterization of shelters of the giant otter (Pteronura brasiliensis, Mammalia, Carnivora, Mustelidae) in the pantanal wetlands, state of Mato Grosso, Brazil
Figure 1. Demarcation of shelters (dens and campsites) and latrines of giant otter in Espírito Santo Creek, Natural Heritage Private Reserve, SESC Pantanal, municipality of Barão de Melgaço, northern portion of Pantanal, state of Mato Grosso, Brazil.
Characterization data for the manuscript: "Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF"
<p>Visualize the data in this dataset: <a href="https://www.c6h6.org/zenodo/record/?id=7186602">open entry</a>.</p>
Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure
<p>Genome-wide association study summary statistics.</p>
Evidence of Jurassic extension in NW Argentina: Characterization of fault-related strata at the Salta Group base using sandstone provenance and zircon U–Pb geochronology
<p>Supporting information accompanying the publication "Evidence of Jurassic extension in NW Argentina: Characterization of fault-related strata at the Salta Group base using sandstone provenance and zircon U–Pb geochronology" published in Journal of South American Earth Sciences. The dataset contains information on analytical procedures of detrital zircon U-Pb analyses.</p>
Supporting Data of Bonadonna et al. "Physical Characterization of Long-Lasting Hybrid Eruptions: the Tajogaite Eruption of Cumbre Vieja (La Palma, Canary Islands)"
<p>Data on plume height, volcanic tremor, deposit thinning and lava emissions of Bonadonna et al. "Physical Characterization of Long-Lasting Hybrid Eruptions: the Tajogaite Eruption of Cumbre Vieja (La Palma, Canary Islands)"</p>
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</p>
Characterization factors for fisheries
<p>Supporting information of </p> <p>Hélias A, Langlois J, Fréon P. Fisheries in life cycle assessment: Operational factors for biotic resources depletion. Fish Fish. 2018. https://doi.org/10.1111/faf.12299.</p> <p>Use by default the Characterisation Factors given in sheet "Stocks CF (by FAO Area)", except if the localisation of the fisheries is unknown (use the sheet "world aggregated CF") and/or the species is not exactly identified (use the sheet "World & ISSCAAP aggregated CF")</p> <p>Version:1 (november 2017)<br> <br> <br> <br> <br> <br> Contact:arnaud.helias@supagro.fr<br> <br> <br> <br> <br> </p>
Supporting dataset: "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer"
<p>Included in this repository are supporting field data and final model input files used to produce the results of the manuscript:</p> <p>Trglavcnik, V., Morrow, D., Weber, K. P., Li, L., & Robinson, C. E. (2017), "Analysis of tide and offshore storm-induced water table fluctuations for structural characterization of a coastal island aquifer."</p> <p>This dataset contains:</p> <ul> <li>SableIsland_data.xlsx <ul> <li>Data used to produce figures in the above manuscript. </li> </ul> </li> <li>Final_SS.zip <ul> <li>Input files for the final model (see Figure 3 in manuscript), steady-state SEAWAT simulation. </li> </ul> </li> <li>Final_PBC.zip <ul> <li>Input files for the final model, transient SEAWAT simulation with a sinusoidal tidal boundary implemented by the Periodic Boundary Condition package (developed for MODFLOW by Post, 2011).</li> </ul> </li> </ul> <p>All data and files are licensed under Creative Commons Attribution Share Alike 4.0 International.</p> <p> </p> <p> </p>
Deep microbiome-based characterization of the alterations in resident bacterial communities of pasteurized bovine milk contaminated with Salmonella Typhimurium over time
Open the record for dataset details and reuse information.
Shear strength characterization and statistical modelling of 12 hardwood timber species from the Congo Basin
Open the record for dataset details and reuse information.
Anion exchange chromatography-mass spectrometry to characterize proteoforms of alpha-1-acid glycoprotein during and after pregnancy
<p><span>This data repository contains all previously unpublished raw data files for the manuscript "Anion exchange chromatography-mass spectrometry to characterize proteoforms of alpha-1-acid glycoprotein during and after pregnancy"</span></p>
Phenotypic characterization of Histoplasma species
<p>Histoplasmosis is an endemic mycosis that often presents as a respiratory infection in immunocompromised patients. Hundreds of thousands of new infections are reported annually around the world. The etiological agent of the disease, <em>Histoplasma, </em>is a dimorphic fungus commonly found in the soil where it grows as mycelia. Humans can become infected by <em>Histoplasma</em> through inhalation of its spores (conidia) or mycelial particles. The fungi transitions into the yeast phase in the lungs at 37°C. Once in the lungs, yeast cells reside and proliferate inside alveolar macrophages. Genomic work has revealed that <em>Histoplasma</em> is composed of at least five cryptic phylogenetic species that differ genetically. Three of those lineages have received new names. Here we evaluated multiple phenotypic characteristics (colony morphology, secreted proteolytic activity, yeast size and growth rate) of strains from five of the phylogenetic species of <em>Histoplasma</em> to identify phenotypic traits that differentiate between these species: <em>H. capsulatum</em> <em>sensu stricto</em>, <em>H. ohiense</em>, <em>H. mississippiense</em>, <em>H. suramericanum</em>, and an African lineage. We report diagnostic traits for three species. The other two species can be identified by a combination of traits. Our results suggest that 1) there are significant phenotypic differences among the cryptic species of <em>Histoplasma</em>, and 2) that those differences can be used to positively distinguish those species in a clinical setting and for further study of the evolution of this fungal pathogen.</p>
Synthesis and characterization of fluorescent silica nanoparticles DATASET
<p>The synthesis of fluorescent nanoparticles has many potential applications in bio-imaging for enhanced and safer cell marking. Herein, two Stöber synthesis pathways were explored to incorporate fluorescein and rhodamine B in silica nanoparticles. Their luminescence properties were studied and probed with fluorescence spectroscopy. Mixed results were obtained. Simultaneous synthesis of nanoparticles and addition of fluorescent molecules seems to be the more promising pathway.</p> <p> </p>
Dataset: In-vivo characterization of magnetic inclusions in the subcortex from non-exponential transverse relaxation decay
<p>This repository includes the data used to compile the results presented in the scientific publication: "In-vivo characterization of magnetic inclusions in the subcortex from non-exponential transverse relaxation decay".</p> <p>Rita Oliveira, Antoine Lutti<br>Laboratory for Research in Neuroimaging (LREN)<br>Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne<br>Mont-Paisible 16, CH-1011 Lausanne, Switzerland<br><br>Classically, the MRI transverse relaxation decay is analyzed by fitting the signal decay over echo time voxel-wise with a monoexponential function (Exp), for which a decay rate R<sub>2</sub><sup>∗</sup> is estimated. However, the presence of magnetic material within the tissue, such as iron-loaded cells, myelin, or blood vessels, introduces variations in the magnetic field, which can modify the exponential behaviour of the decay (1,2). In such inhomogeneous magnetic fields, the theory predicts a transient regime starting with a Gaussian behaviour at short echo times and approaching a monoexponential relaxation at long echo times (1,3–6).<br>We highlight three different analytical descriptions of the signal decay that account for the transient regime of the transverse relaxation decay: i) the Anderson and Weiss, 1953 model (AW); ii) the Jensen and Chandra, 2000 model/Sukstanskii and Yablonskiy, 2003 model (SY; in the article is called JC); iii) and following a Padé approximation (Padé) of the transition from Gaussian to exponential decay.<br>This repository includes transverse relaxation decay data that enables the observation of the non-exponential MRI transverse relaxation. The data was acquired from 5 healthy volunteers at 3T. AW, SY, Padé, and Exp are the different methods that we used to fit the data with. Here we focus on the analysis of subcortical brain regions: Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus.</p> <p><strong>Data Description</strong><br>The necessary files to compile the results presented in the scientific publication can be found in the ‘<em>multiecho</em>’ folder. There are three different folders corresponding to three repetitions of the acquisition (‘rep1’ to ‘rep3’). The data consists of:<br>• resc_den_ subject_name_N.nii: magnitude image file corresponding to echo N. These files were previously denoised and rescaled (resc_den). The description field of the header of the images contains the corresponding TE at which the image was acquired, which will be needed in the fitting routine. Since we focus on the analysis of subcortical brain regions (Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus), the multi-echo data is masked within this region.<br>• nf: value of the noise floor level. Corresponds to the noncentrality parameter of a Rician distribution fitted to the background signal.</p> <p>In the ‘<em>anat</em>’ folder the user has access to:<br>• MT: Magnetization Transfer map (MTsat) that serves as a reference anatomical image.<br>• ROI folder: contains masks of each of the 5 regions of interest analyzed in the scientific paper: Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus.</p> <p><br>The ‘<em>modelfits</em>’ folder contains pre-computed results for each subject analyzed. If the user uses the analysis code that comes along with this dataset (<a href="https://github.com/LREN-physics/TransverseRelaxation">https://github.com/LREN-physics/TransverseRelaxation</a>), this folder will be overwritten with the new results. For each method (‘AW’, ‘SY’, ‘Pade’, ‘Exp’) there is a folder containing the corresponding resulting maps. These maps are:<br>• R2s.nii: map of R<sub>2,micro</sub><sup>∗</sup> [ms<sup>-1</sup>] for ‘AW’, ‘SY’, and ‘Pade’ options. Map of R<sub>2</sub><sup>∗</sup> [ms<sup>-1</sup>] for ‘Exp’ fit.<br>• OmegaSq.nii: map of 〈\(\Omega^2\) [rad<sup>2</sup> ms<sup>-2</sup>]. Not available for ‘Exp’ fit.<br>• TE0signal.nii: map of the initial signal amplitude S<sub>0</sub>.<br>• T2mol.nii: map of the inverse of effective transverse relaxation rate resulting from processes on the nanoscale [ms]<br>• AIC.nii: map of Akaike information criterion regarding the fitting procedure.<br>• MSE.nii: maps of the mean square error of the fitting procedure.<br>• DataMatrix.mat: matrix containing the data used for the fitting procedure.<br>• VoxelIndices.mat: vector containing the indices of the voxels corresponding to the analyzed data, which is restricted to the subcortical regions.<br>• Params.mat: structure containing the parameters used for the analysis.<br>Inside ‘modelfits’ there are also two folders corresponding to two different regimes that can describe the transverse relaxation decay: static dephasing regime (‘SDR’) or diffusion narrowing regime (‘DNR’). Under the assumption of SDR, we computed:<br>• ki_ppm.nii: maps of 𝛥𝜒, which is the difference in susceptibility of the magnetic inclusions to the surrounding tissue [addimentional, in ppm and in SI units]<br>• zeta.nii: maps of 𝜁, which is the volume fraction of the magnetic inclusions [addimentional]<br>Under the assumption of DNR, we computed:<br>• alpha.nii: 𝛼=𝜏〈\(\sqrt{\Omega^2}\)〉 [addimentional]<br>• tau_ms.nii: maps of 𝜏, which is the time scale for water molecules to diffuse away from magnetic inclusions [ms]<br>Please refer to the corresponding article for a complete description of the methods and corresponding estimated parameters.</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>
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.