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230 results for “Data Aggregation”
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>
Data Files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity"
<p>Data files for "Subsidies for Close Substitutes: Aggregate Demand for Residential Solar Electricity" [https://doi.org/10.1016/j.euroecorev.2024.104848]. Findings of the paper can be replicated using these data files, along with code at https://github.com/xabajian/AP_Solar/. Please contact Alexander Abajian <xander.abajian@gmail.com> with any questions regarding the enclosed files.</p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
Spreadsheet for analysis of illness-death model with aggregated data
<p>Spreadsheet for calculation of a recurrence equation and analysis of fixed points in the illness-death model.</p>
Data for "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer"
<p>These are the data associated with the paper, "Unsupervised learning of sequence-specific aggregation behavior for a model copolymer" (DOI 10.1039/D1SM01012C). Each of the directories contains subdirectories with `GSD` files dumped from HOOMD. Each subdirectory roughly corresponds to one or two of the figures in the paper.</p>
Probiotic Bacillus subtilis Protects against a-Synuclein Aggregation in C. elegans (fluorescence microscopy data)
<p>This project has been submitted by the Maria Doitsidou Lab.<br> <br> Project contents:<br> This project contains datasets of z-stack images of <em>C. elegans</em> strains used to study how the gut microbiome affects Parkinson’s disease. Each strain contains a chromosomal insertion containing YFP fused to α-synuclein (pkIs2386[Punc-54::α-synuclein::YFP + unc-119(+)]). The following<em> C. elegans</em> strains were used and/or created for this project:<br> NL5901 pkIs2386[Punc-54::α-synuclein::YFP + unc-119(+)]<br> MDH586 daf-2(e1370) III; pkIs2386<br> MDH585 daf-16(mu86) I; pkIs2386<br> MDH587 hsf-1(sy441) I; pkIs2386<br> MDH657 daf-2(e1370) III; daf-16(mu86) I; pkIs2386<br> MDH614 daf-2(gk390525) III; pkIs2386<br> MDH611 eat-2(ad465) II; pkIs2386<br> MDH711 lagr-1(gk331) I, pkIs2386<br> MDH725 sptl-3(ok1927) II; pkIs2386<br> MDH724 asm-3(ok1744) IV; pkIs2386.<br> <br> High magnification (40x objective) z stack images of the head region were obtained by using a Zeiss Axio imager 2 microscope.<br> <br> <br> Aim:<br> Study how a probiotic<em> B. subtilis</em> strain affects alpha-synuclein protein aggregation.<br> <br> Main results:<br> The authors showed that the probiotic<em> B. subtilis</em> strain PXN21 inhibits and clears a-synuclein aggregation in a <em>C. elegans </em>model. The bacterium acts via metabolites and biofilm formation to activate protective pathways in the host, including DAF-16/FOXO and sphingolipid metabolism.<br> <br> Contributors:<br> Maria Eugenia Goya, Feng Xue, Cristina Sampedro-Torres-Quevedo, Sofia Arnaouteli, Lourdes Riquelme-Dominguez, Andres Romanowski, Jack Brydon, Kathryn L. Ball, Nicola R. Stanley-Wall and Maria Doitsidou<br> <br> These datasets were used in the following publication:<br> <br> Probiotic Bacillus subtilis Protects against a-Synuclein Aggregation in <em>C. elegans</em><br> <br> Maria Eugenia Goya, Feng Xue, Cristina Sampedro-Torres-Quevedo, Sofia Arnaouteli, Lourdes Riquelme-Dominguez, Andres Romanowski, Jack Brydon, Kathryn L. Ball, Nicola R. Stanley-Wall and Maria Doitsidou<br> <br> Cell Reports January 14, 2020 30 367-380; first published January 14, 2020 <a href="https://doi.org/10.1016/j.celrep.2019.12.078">https://doi.org/10.1016/j.celrep.2019.12.078</a></p>
Supporting data for "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics"
<p>Numerical settings, routines and post-processed data used to generate the figures presented in "The time scale of shallow convective self-aggregation in large-eddy simulations is sensitive to numerics", manuscript submitted to Journal of Advances in Modeling Earth Systems. This version is an update after accounting for comments of three reviewers to the submitted manuscript.</p>
Aggregated PM10 data for Europe
<p>JSON-formatted and z-standard compressed data on PM10 emissions in the EU since 2013.</p>
Data for: Fungal parasitism on diatoms alters formation and bio–physical properties of sinking aggregates: Particle analyses
<p>Phytoplankton forms the base of aquatic food webs and element cycling in diverse aquatic systems. The fate of phytoplankton-derived organic matter, however, often remains unresolved as it is controlled by complex, interlinked remineralization and sedimentation processes. We here investigate a rarely considered control mechanism on sinking organic matter fluxes: fungal parasites infecting phytoplankton. We demonstrate that bacterial colonization was promoted 3.5-fold on fungal-infected phytoplankton cells in comparison to non-infected cells in a cultured model pathosystem (diatom <em>Synedra</em>, fungal microparasite <em>Zygophlyctis</em>, and co-growing bacteria), and even ≥17-fold in field-sampled populations (<em>Planktothrix</em>, <em>Synedra</em>, and <em>Fragilaria</em>). The <em>Synedra</em>–<em>Zygophlyctis</em> model system further revealed that fungal infections reduced the formation of aggregates. Moreover, carbon respiration was 2-fold higher and settling velocities 11–48% lower for similar-sized fungal-infected <em>vs</em> non-infected aggregates. Our data imply that parasites can effectively control the fate of phytoplankton-derived organic matter on a single-cell to single-aggregate scale, potentially enhancing remineralization and reducing sedimentation in freshwater and coastal systems.</p>
Main text figure data and scripts for "Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach"
<p>(as README.txt):</p> <p>Main text figure data and scripts for “Simulating optical linear absorption for mesoscale molecular aggregates: an adaptive hierarchy of pure states approach”, by Tarun Gera, Lipeng Chen, Alex Eisfeld, Jeffrey R. Reimers, Elliot J. Taffet and Doran I. G. B. Raccah.</p> <p>Each directory is dedicated to a particular figure published in the paper. In each directory there are sub-directories which contains the data plotted in each panel. Each data file is a 2-D list in the format of (x,y) for each plot. There are python scripts (Fig_X.py) in each directory to plot the data.</p> <p>Table of contents:</p> <p>Figure_2:</p> <p> - 4_site_edge_contri.npy: Calculated edge sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_inner_contri.npy: Calculated inner sites contribution to the total absorption spectrum for a 4-site chain system v/s energy. <br> - 4_site_total_spectra.npy: Calculated total absorption spectrum for a 4-site chain system v/s energy. </p> <p><br> Figure_3:</p> <p>Panel A:<br> <br> - Mean_Error_Edge.npy: Mean error for the edge case v/s number of trajectories.<br> - Mean_Error_Inner.npy: Mean error for the inner case v/s number of trajectories.<br> - Mean_Error_SS.npy: Mean error for a single site initial condition v/s number of trajectories.<br> - Mean_Error_GD.npy: Mean error for a 4-site chain system with Gaussian distributed site energies v/s number of trajectories.</p> <p>Panel B: </p> <p> - Scaled_error_SS.npy: Mean error for a single site initial condition normalized by the square-root of one v/s number of trajectories.<br> - Scaled_error_PS.npy: Mean error for a pair site initial condition normalized by the square-root of two v/s number of trajectories.<br> - Scaled_error_AS.npy: Mean error for an all site initial condition normalized by the square-root of four v/s number of trajectories.</p> <p>Figure_4: </p> <p>Panel_A:</p> <p> - List_Error.npy: Calculated mean error for a 4-site chain for a set of auxiliary error bounds.</p> <p>Panel_B:</p> <p> - Cw_4S_HOPS.npy: Absorption spectrum for a 4-site chain calculated using dyadic HOPS v/s energy.<br> - Cw_4S_DadHOPS.npy: Absorption spectrum for a 4-site chain calculated using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_12S_DadHOPS.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS without including state adaptivity v/s energy.<br> - Cw_12S_DadHOPS_SA.npy: Absorption spectrum for a 12-site chain calculated using DadHOPS with state adaptivity v/s energy.</p> <p>Panel_D:</p> <p> - Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-pigment system.<br> - Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-pigment system.<br> - N_states_DadHOPS.npy: Number of site states required to run a DadHOPS calculation for each N-pigment system.<br> - N_states_HOPS.npy: Number of site states required to run a dyadic HOPS calculation for each N-pigment system.<br> </p> <p>Figure_5:<br> <br> Panel_C: </p> <p> - PSI_Cw_HEOM.npy: PSI absorption spectrum calculated using HEOM v/s energy.<br> - PSI_Cw_HOPS.npy: PSI absorption spectrum calculated using dyadic HOPS v/s energy.</p> <p>Panel_D:</p> <p> - PSI_Error_Random.npy: Calculated mean error, where clusters of 4 were assigned randomly v/s number of trajectories.<br> - PSI_Error_Coupling.npy: Calculated mean error, where clusters of 4 were assigned based on electronic coupling values v/s number of trajectories.</p> <p><br> Figure_6:</p> <p>Panel_A:</p> <p> - PBI_Exp_data_dil.npy: Experimental data for a dilute solution of PBI v/s energy.<br> - PBI_Cw_DadHOPS_300.npy: Calculated spectrum for a PBI monomer with the spread in static disorder of value 300 cm^{-1} v/s energy.<br> - PBI_Cw_DadHOPS_400.npy:: Calculated spectrum for a PBI monomer with the spread in static disorder of value 400 cm^{-1} v/s energy.</p> <p>Panel_B:</p> <p> - PBI_Exp_data_conc.npy: Experimental data for a concentrated solution of PBI v/s energy.<br> - PBI_trimer_Cw_DadHOPS.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.</p> <p>Panel_C: </p> <p> - Cw_PBI_monomer.npy: Calculated spectrum for a PBI monomer using DadHOPS v/s energy.<br> - Cw_PBI_dimer.npy: Calculated spectrum for a PBI dimer using DadHOPS v/s energy.<br> - Cw_PBI_trimer.npy: Calculated spectrum for a PBI trimer using DadHOPS v/s energy.<br> - Cw_PBI_heptamer.npy: Calculated spectrum for a PBI heptamer using DadHOPS v/s energy.<br> - Cw_PBI_1000mer.npy: Calculated spectrum for a PBI 1000mer using DadHOPS v/s energy.</p> <p>Panel_D:</p> <p> - peak_00_position.npy: relative position of the 00 peak for different number of pigments.<br> - peak_00_position_1000.npy: relative position of the 0,0 peak for a system with 1000 pigments. (Single value file)<br> - peak_I_ratio.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for different number of pigments.<br> - peak_I_ratio_1000.npy: ratio of intensities of peak 0,1 w.r.t peak 0,0 for a system with 1000 pigments. (Single value file)</p> <p><br> Figure_7:</p> <p> - PBI_N_states_DadHOPS.npy: Number of states required to run a DadHOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_HOPS.npy: Number of auxiliary states required to run a dyadic HOPS calculation for each N-PBI molecules system. <br> - PBI_Aux_states_DadHOPS.npy: Number of auxiliary states required to run a DadHOPS calculation for each N-PBI molecules system. </p> <p>The packaged scripts may be run with Python 3.10 and the associated versions of the os, numpy, and matplotlib packages. <br> </p>
Data set of simulated rimed aggregates for "A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation"
<p><strong>Simulated rimed aggregates</strong> generated with https://github.com/jleinonen/aggregation in setting "aggregation followed by riming".</p> <p>Aggregates were built from between 10 to 700 monomer crystals of <strong>columns, dendrites, needles, plates or rosettes</strong> with mean sizes of 100 or 200 micrometer. Then they were exposed to ELWP = 2.0 kg m⁻². Monomer crystals are composed of cubical elements with resolution 20 micrometer. Frozen rime droplets are also represented by 20 micrometer cubes.</p> <p>The data set contains folders with <strong>evolution (evol) and shape files for each monomer crystal type</strong>. For each particle one evolution and one corresponding shape file exists. The evolution (evol) file contains particle mass, rime mass, area, size, fall speed (Heymsfield&Westbrook, 2010), fall speed (Khvorostyanov&Curry, 2005) for each step during the aggregation and riming process. The corresponding shape file contains the x,y,z positions of the cubical elements that compose the particle for each step. <strong>For further documentation see readme.</strong></p>
Data from: Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events
<p>This data accompanies the paper entitled <em>Evolution of Large Aβ16-22 Aggregates at Atomic Details and Potential of Mean force Associated to Peptide Unbinding and Fragmentation Events</em></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: the first one with 139 <em>Aβ16-22 </em><em>peptides, the second one with 106 peptides.</em><em> </em>Each system has been simulated at 300 K. Starting configurations of the peptides are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the peptides every 100 ps are provided for all the systems in XTC gromacs format. For system 1 we also provide XTC trajectories for all the replicas of the REST2 simulation.</p>
Data and Software for "Numerical diffusion and Turbulent mixing in convective self-aggregation"
<p>This folder contain the python scripts and the data necessary for reproducing figures and results reported in the paper " Numerical diffusion and turbulent mixing in convective self-aggregation" (in preparation for submission for the Journal of Advances in Modeling Earth System)</p>
Data from: Controlled molecular arrangement of easily aggregated deoxycholate with layered double hydroxide
<p><span>Deoxycholate (DA) is a natural emulsifying agent involved in the absorption of dietary lipids. Due to the facial distribution of hydrophobic-hydrophilic region, DA easily aggregates under ambient conditions, and this property hinders the practical application of DA in clinical application. In this study, we found that the molecular arrangement of DA molecules could be controlled by utilizing layered double hydroxide (LDH) under a specific reaction condition. The effect of reaction methods such as co-precipitation, ion exchange, and reconstruction on the molecular arrangement of DA was investigated by X-ray diffraction, Fourier-transform infrared spectroscopy, high-resolution transmission electron microscopy, and differential scanning calorimetry. It was demonstrated that the self-aggregation of DA molecules could be suppressed by the oriented arrangement of DA between the gallery space of LDH. The DA moiety was well stabilized in the LDH layers due to the electrostatic interaction between DA molecules and LDH layers. The most ordered arrangement of DA molecules was observed when DA was incorporated into LDH via a reconstruction method. The DA molecules arranged in LDH via reconstruction did not show significant exothermic nor endothermic behavior up to 400</span><span>℃</span><span>, showing that the DA moiety lost its intermolecular attraction in between LDH layers.</span></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>
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>
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>
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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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
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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
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