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9,152 results for “Networking”
Distributed predictive QoS in presence of network- and mobility-related drifts
<p>The datasets represent a dynamic environment, where several client-vehicles are moving in an urban area. Each client runs a streaming cloud service constantly receiving data packets. Network simulation is performed using Simu5G, a library that emulates a 5G cellular environment in OMNeT++. The simulator's radio parameters are set according to the Macro-cell model proposed by International Telecommunication Union. The map comprises of an urban 600x600 square meters area located in a suburb of a European capital. Inside this area four 5G base-stations (gNodeBs) have been installed by the national network operator, enabling four 5G cells. This area, divided into several blocks by the actual road network is integrated in our simulation by an OpenStreetMap (OSM) instance. The total number of included vehicles is set to 25, according to vehicle density statistics in the corresponding country. The road network's traffic is simulated by SUMO that creates a digitized version of the (real-world) OSM map and produces the route files for the vehicles. Route files are loaded in the Simu5G simulator, where a network-vehicular mobility co-simulation takes place. For each vehicle's route we assume SUMO's default parameters for urban environment: exponential speed model (with maximum speed restriction as defined by the OSM traffic rules) and the probability matrix at intersections for {lane keeping, turn left and right} as {0.5, 0.25 and 0.25}, respectively. The following information is collected for each vehicle using OMNeT++'s monitoring service: timestamp, channel quality indicator, packet delay, measured signal to noise ratio (SNR), client position (x,y,z), client velocity (x,y,z), received SNR, radio link control throughput, serving cell, client throughput. These features are sampled at 1 Hz and comprise the values of our synthetic time-series QoS dataset. </p> <p>We have created two drift datasets that correspond to complementary cases of major long-term changes in the considered environment: 1) a network infrastructure-driven scenario (Sc1) and 2) a human behavior-driven scenario (Sc2). In Sc1 we assume that two out of four gNodeBs are switched off under a cost-reduction on/off policy or an infrastructure-share strategy (adopted by MNOs) that would imply such changes. For Sc2 we modify the users' mobility pattern; we assume that a "hotspot" e.g., a metro station is created in the lower-right edge of the map resulting in a traffic increase to that area. This is achieved by increasing the probabilities of the routes leading to the "hotspot" in SUMO's route planning. All generated datasets have a total duration of 20 hrs (simulation time) and the respective drift event is introduced at t=10 hrs. </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (parameters, trajectories)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> <br> 1. Stripped parameter file *.parm7. <br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant simulation files:</li> </ul> <p> 1.cMD(Classical MD simulation):<br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.<br> <br> <br> 2.GaMD(Gaussian Accelerated MD simulation): <br> 1. Stripped parameter file *.parm7.<br> 2. Simulation file after removing ions and water and merging last 5us of production run in amber *.nc format.</p>
Data from: High foraging fidelity and plant-pollinator network dominance of non-native honeybees (Apis mellifera) in the Ecuadorian Andes
<p>These data reflect the floral visitor survey and mark-recapture efforts used in the 2022 study, "High Foraging Fidelity and Plant‑Pollinator Network Dominance of Non‑native Honeybees (<em>Apis mellifera</em>) in the Ecuadorian Andes"</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p>
Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks
<p>This dataset comes from the following paper:</p> <p>Lorenzo Bonetti, Aron Cobianchi, Daniele Natali, Stefano Pandini, Massimo Messori, Maurizio Toselli, Giulia Scalet, Solvent-triggered shape change in gradient-based 4D printed bilayers: case study on semi-crystalline polymer networks, Soft Matter, 2024. <a href="https://doi.org/10.1039/D4SM00304G" target="_blank" rel="noopener">https://doi.org/10.1039/D4SM00304G</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>.xls files of the experimental data</li> <li>. m of the theoretical computations</li> </ul>
Deciphering complex networks via topology-encoded latent hyperbolic geometry
<p><span>Complex networks, which are the abstraction of many real-world systems, present a persistent challenge across disciplines for people to decipher their underlying information. Recently, hyperbolic geometry of latent spaces has gained traction in network analysis, due to its ability to preserve certain local intrinsic properties of the nodes. In this study, we explore the problem from a much broader perspective: understanding the impact of nodes' global topological structures on latent space placements. Our investigations reveal a direct correlation between the topological structure of nodes and their positioning within the latent space. Building on this deep and strong connection between node distance and network topology, we propose a novel embedding framework called Topology-encoded Latent Hyperbolic Geometry (TopoLa) for analyzing complex networks. With the encoded topological information in the latent space, TopoLa is capable of enhancing both conventional and low-rank networks, using the singular value gap to clarify the mathematical principles behind this enhancement. Meanwhile, we show that the equipped TopoLa distance can also help augment pivotal deep learning models encompassing knowledge distillation and contrastive learning.</span></p>
Neural networks and surface models of Itokawa and Bennu
<p>This dataset comprises the neural networks and surface models of asteroids Itokawa and Bennu, as presented in the paper "Asteroid-NeRF: A deep-learning method for 3D surface reconstruction of asteroids" by Shihan Chen, Bo Wu, Hongliang Li, Zhaojin Li, and Yi Liu.</p>
Neural network potentials for the phosphoester bond formation between phosphate and methanol in water
<p>Neural Network Potentials for H2PO4 and HPO4, as well as the training sets used to train them. </p> <p>The energies are in eV, the forces in eV/angstrom, the xyz coordinates in angstrom, and the box size in angstrom. </p> <p><br>For H2PO4: </p> <p>Number of structures = 386457</p> <p>Number of atoms = 397</p> <p><br><a href="../api/records/11120695/draft/files/coord_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">- coord_H2PO4.npy</a>: npy array containing the xyz coordinates. Array size = (number of structures, number of atoms * 3)</p> <p>- <a href="../api/records/11120695/draft/files/box_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">box_H2PO4.npy</a>: npy array containing the box sizes. Array size = (number of structures, 9)</p> <p>- <a href="../api/records/11120695/draft/files/force_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">force_H2PO4.npy</a>: npy array containing the atomic forces. Array size = (number of structures, number of atoms * 3)</p> <p>- <a href="../api/records/11120695/draft/files/energy_H2PO4.npy/content" target="_blank" rel="noopener noreferrer">energy_H2PO4.npy</a>: npy array containing the full box potential energy. Array size = (number of structures)</p> <p>- <a href="../api/records/11190726/draft/files/type_HPO4.npy/content" target="_blank" rel="noopener noreferrer">type_H2PO4.npy</a>: npy array containing the atom types. Array size = (number of atoms)</p> <p>- <a href="../api/records/11190726/draft/files/type_HPO4.txt/content" target="_blank" rel="noopener noreferrer">type_H2PO4.txt</a>: text file containing the correspondance between the type index, as given in <a href="../api/records/11190726/draft/files/type_HPO4.npy/content" target="_blank" rel="noopener noreferrer">type_H2PO4.npy</a> and the atom name</p> <p>- <a href="../api/records/11120695/draft/files/graph_1_000_compressed_H2PO4.pb/content" target="_blank" rel="noopener noreferrer">graph_1_000_compressed_H2PO4.pb</a>: neural network potential to propagate trajectories.</p> <p>The nomenclature is the same for HPO4.</p> <p>Number of structures = 227881</p> <p>Number of atoms = 396</p>
Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)
<p>The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments. </p> <p>The data folder has three subfolders:</p> <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> <p>The data folder has to be integrated in the Git repository nn-toc, to execute the code.</p> <p> </p> <p> </p>
Mental Health in Social Networks with Machine Learning Algorihtms
<p>The <strong>DatasetMH.xlsx</strong> excel corresponds to a corpus of mental health in social networks labelled with polarity and stigma. In particular, the corpus consists of 2,287 comments labelled with polarity (positive, negative, neutral) and stigma from comments on Instagram posts about celebrity mental health disclosures:</p> <ol> <li>Polarity: It consists of giving a positive, negative or neutral/undefined value to the comments in response to the disclosure or description of the symptomatology in the post. Positive polarity reflects understanding, encouragement or even admiration of the publication. E.g., “Cheer up, we love you".”. Negative polarity is assigned when the person expresses negative opinions, usually questioning the post with ironic, sarcastic or even mocking and disparaging comments. E.g., “how you show that you don't know what depression or anxiety is, shame on you!”. Neutral or undefined polarity is assigned in cases where no clear opinion is detected or can be interpreted in both directions. E.g., “take medication, it will help you" "and your partner?”</li> <li>Stigma: stigmatising responses to comments are behaviours in which negative beliefs and emotions towards MH problems are expressed. Stigma manifests in a variety of forms including rejection and anger against the person, which may extend to contempt or mockery, belittling their problem. E.g.,"What a desire to draw attention to yourself"; "what you have is a story"; "you're so inconsistent and seeking the limelight". Because socially we know that "stigma is wrong" many rejection comments are made in an ironic or sarcastic way. E.g., and how do you write on insta?"; "better information from someone who doesn't have a current account". Additionally, anger is shown by arguing that such posts "trivialise or commercialise" MH. E.g.,"don't come and tell me your false stories of overcoming, without even knowing what it is to work...". Other times the stigma manifests itself as pity or sorrow for the person. E.g.,“It breaks my heart”; “poor thing”.</li> </ol> <p>The file <strong>DatasetMH_Emotions.xlsx</strong> corresponds to a corpus of mental health in social networks labelled with emotions. In particular, the corpus consists of 2,287 comments labelled with five emotions plus a neutral class from comments on Instagram posts about celebrity mental health disclosures. These emotions are:</p> <ul> <li>Love/admiration: This emotion involves messages where admiration, approval and love are closely related.</li> <li>Gratitude: the messages imply a sincere appreciation for sharing mental health-related content on social networks.</li> <li>Comprehension/empathy/identification: The messages involve interest in and understanding of the message, including self-identification with the situation or context.</li> <li>Sadness: This primary emotion is produced by events that are not pleasant and that denote heaviness. It includes many manifestations of pity for the person.</li> <li>Anger/contempt/mockery: This emotion involves responses of irritation and attacks on the person as ridiculous and superficial.</li> <li>Neutral: This category corresponds to messages without emotions.</li> </ul> <p>The labelling process of both datasets was divided into two phases: an initial phase with a pilot corpus (N = 787 comments) and a second phase focused on the development of the corpus with all the comments of the selected posts (N = 21151). The same methodology was followed in both phases: once the comments were collected, the corpus was cleaned, and then two independent experts were responsible for labelling each category. A third expert then reviewed the comments to resolve discrepancies. In the third and final phase, a final corpus for application to the machine learning algorithms is built from the large corpus (N = 2287).</p> <p>Classification models are a set of machine learning algorithms developed to assess emotional response, i.e. polarity, stigma and emotions in social networks, based on previously developed datasets (<strong>DatasetMH_Emotions.xlsx</strong>, <strong>DatasetMH.xlsx</strong>).</p>
Numerical methods for neurobiological networks - Accompanying Data
<p>Data supporting the following tutorial</p> <p>https://github.com/danieleavitabile/numerical-analysis-mathematical-neuroscience</p>
Forest structure and heterogeneity increase diversity and alter the composition of host-parasitoid networks
<p>Antagonistic host-parasitoid interactions can be quantified using bipartite and meta networks, which have the potential to reveal how habitat structural elements relate to this important ecosystem function. Here, we analysed the host-parasitoid interactions of cavity-nesting bees and wasps, as well as their abundance, diversity, and species richness with forest structural elements from 127 forest research plots in southwestern Germany. We found that parasitoid abundance, diversity, and species richness all increase with host abundance, a potential mediator between parasitoids and forest structure. Both parasitoid abundance and diversity increased with stand structural complexity, possibly mediated by the abundance of hosts. Additionally, parasitoid abundance increased with increasing standing deadwood and herb cover. The bipartite networks of host-parasitoid interactions showed higher connectance with increasing standing deadwood, herb cover, and host abundance. Analyses of interactions within the host-parasitoid metanetwork revealed that increasing host abundance and decreasing canopy cover diversify the suites of interactions present at the plot level. These results demonstrate that forest structural elements can improve the stability and resilience of host-parasitoid networks by promoting parasitoids and diversifying interactions in ecological networks.</p>
Impact of background input on memory consolidation in In-Vitro neural networks
<p>Memory consolidation is a complex process, that can be divided into two stages: first, memories are temporary stored in hippocampus and in the second stage, repeated replay slowly transfers memories to the neo-cortex for long-term consolidation. This 2<sup>nd</sup> stage occurs during slow wave sleep, a phase characterized in the cortex by low cholinergic tone and low afferent input. A recent in-vitro study showed that high cholinergic tone hampers memory consolidation, probably due to lowered network excitability (defined as the mean network response to one neuron spiking). Here we investigate whether low background input contributes to memory consolidation.</p> <p>We used cortical neuronal networks on multi electrode arrays to study memory. When input deprived, these networks develop an activity-connectivity balance. Focal stimuli initially disrupt the existing balance, inducing connectivity changes. When repeated, this effect fades and the response becomes part of spontaneous patterns (memory formation). Application of the same stimulus hours later does not affect connectivity indicating that memory was consolidated.</p> <p>We applied five periods (10 min each) of focal electrical stimulation at different electrodes (A B A), separated by 1 hour of spontaneous activity . Some cultures were transfected to express channelrhopsins (ChR2) enabling global optogenetic background stimulation. We used 12 control, 15 ChR2 cultures with no background input and 8 ChR2 cultures with superimposed random optogenetic stimulation during electrical stimulation periods (f<sub>mean</sub>=5 Hz) to mimic afferent input.</p> <p>Background stimulation acutely reduced network excitability during stimulation without persisting effects after cessation. ChR2 cultures showed significantly more dispersed spiking outside network bursts, and network excitability tended to be lower than in control cultures. Stimulation at electrodes A and B induced memory traces in control cultures. Return to electrode A did not further affect connectivity, showing that memory trace A had been consolidated. Background stimulation impeded the formation of memory traces following electrical stimulation at either electrode. ChR2 expression alone also obstructed memorization.</p> <p>These findings confirm the importance of low background afferent input for memory consolidation. The presence of background afferent inputs reduced network excitability, similar to high cholinergic tone. This leads to the conclusion that sufficient network excitability is crucial for memory consolidation, and high network excitability may be a critical feature of slow wave sleep that makes it more suitable for memory consolidation than the awake state.</p>
Data supporting the manuscript "Human adolescent brain network development is different for paralimbic versus neocortical zones"
<p>This data supports the manuscript "Human adolescent brain network development is different for paralimbic versus neocortical zones" by Dorfschmidt et al. All code used in the analysis can be found here: https://github.com/LenaDorfschmidt/morphometric-similarity-adolescence/</p>
Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts
<p>In this repository are all the data/scripts necessary to reproduce the results of the article 'Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts.'</p> <p>The files in NetCDF format and nomenclature <strong>TYPE</strong>_<strong>FORECAST</strong>_202201_202312.nc are the input data for the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain all the predictions from the atmospheric models and the 'observations.' This input data was divided into training, validation, and test periods.</p> <p>The files in NetCDF format and nomenclature <strong>CNN</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the output data of the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain the TMAX and TMIN forecasts for the test period.</p> <p>The files in NetCDF format and nomenclature <strong>MODEL</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the forecasts from the atmospheric models (BRAMS, ETA, WRF, SMEC) and the observations (SAMET) for the same test period. This data was used to compare the predictions of each model with the predictions of the neural network.</p> <p>The file CCN_Unet.py is a Python program containing the configurations of the convolutional neural network with a U-Net architecture used in the study.</p> <p>The files in ASCII format (.py, .gs) are scripts used for visualizing the results and generating the graphs. The other files are shapefiles and masks used for generating figures.</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>
A neural network-based four-body potential energy surface for parahydrogen
<p>We created an isotropic <em>ab initio</em> four-body potential energy surface (PES) for parahydrogen.<br>The energies were calculated using the CCSD(T) method, with an AVDZ atom-centred basis set.</p> <p>This repository contains the input and output files for the ab initio calculations.</p> <p>A detailed description of the data is provided in the README.md file.</p>
Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks
<p> </p> <div> <div><a href="https://arxiv.org/search/cs?searchtype=author&query=Tello,+A">Andres Tello*</a><em>, </em><a href="https://arxiv.org/search/cs?searchtype=author&query=Truong,+H">Huy Truong*</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lazovik,+A">Alexander Lazovik</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Degeler,+V">Victoria Degeler</a>. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks. Engineering Proceedings. 2024; 69(1):50. <a href="https://doi.org/10.3390/engproc2024069050">https://doi.org/10.3390/engproc2024069050</a></div> <br> <div>(*) Both authors contributed equally.<br><br></div> <h2>Update</h2> <div>(04/09/2024): Citation is updated.<br>We have added headers for CSVs and auxiliary data (duration time, edge list, ordered names.. ) in the configuration file (JSON format). As such, corresponding INP files can be omitted when working with this version. <br>The EXN network has been included in this version, so the total number of processed networks is 11.<br>For more details, please read ZENODO_README.md.</div> <h2>Contact</h2> <div>For dataset-related questions: <a href="mailto:h.c.truong@rug.nl" target="_blank" rel="noopener">Huy Truong</a></div> <br> <div>For data acquisition: <a href="mailto:a.tello@rug.nl" target="_blank" rel="noopener">Andres Tello</a></div> <br> <div>If you use this dataset, please cite:</div> <blockquote>@article{tello2024largescale,<br> AUTHOR = {Tello, Andrés and Truong, Huy and Lazovik, Alexander and Degeler, Victoria},<br> TITLE = {Large-Scale Multipurpose Benchmark Datasets for Assessing Data-Driven Deep Learning Approaches for Water Distribution Networks},<br> JOURNAL = {Engineering Proceedings},<br> VOLUME = {69},<br> YEAR = {2024},<br> NUMBER = {1},<br> ARTICLE-NUMBER = {50},<br> URL = {https://www.mdpi.com/2673-4591/69/1/50},<br> ISSN = {2673-4591},<br> DOI = {10.3390/engproc2024069050}<br>}</blockquote> </div>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
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.