Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
9,152
datasets available to search
ShareScore release 0.9.0
Dataset results
9,152 results for “Networking”
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays
<p>First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 5. The periphery of circular area around of artificial creature in each position
<p>It is assumed in the simulation that the artificial creature in each location stands in the center of a circular area with a radius of its vision range, i.e. 2 meters. If the creature turns around itself; it only can see objects in its visual range. When a food object appears in the periphery of this circular area, a creature should find this food. Moving around, another food appears in a random place on periphery of the circular area, and this procedure repeat for 5 iterations. As shown in Figure 5 the periphery of the circular area around the artificial creature in each position is divided to 48 slots, which can be considered as a rectangular. In the simulation program it is implemented as an array of 48 × 3.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 2. Example of different axonal conduction delay between presynaptic neuron A0 and postsynaptic neuron A1 and A2
<p>Different axonal conduction delays between every two neurons are applied as follows:<br> For instance, consider presynaptic neuron A0 and postsynaptic neurons A1 and A2 in Figure 2. If neuron A0 fires spike S0 in t = 9 and S1 in t = 11 and with a time step equal to 0.5 ms, S0 arrives to A1 in t = 13 and to A2 in t = 14 where S1 arrives to A1 in t = 15 and to A2 in t = 16. This is implemented using counters assigned to each spike which increases with each time step in the time window. If counters arrive to axonal delay values, the effect of spikes from presynaptic neuron applies to the post synaptic neurons.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 8. Flowchart for movements of creature
<p>Window time has a fixed length between and and is an appropriate time interval [5]. So in each time window the total number of spikes in each three neurons is compared with other three neurons and artificial creature moves toward direction that the respective neurons fired maximum number of spikes. These fixed time<br> windows consist of 600 time-steps. Each time step is 0.5 ms. Flowchart in Figure 8 shows details.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 1. Typical neural networks SYNAPTIC
<p>A reservoir network has been used in this paper. This structure has been shown in Figure 1.<br> As is observable this network has two input and output layers. The neuronal network that have been<br> used is composed of N = 150 randomly connected Izhikevich spiking neurons and different axonal<br> conduction delays between each two neurons. Information is transferred between neurons of the<br> networks through the links between every two neurons representing synapses. Each neuron is<br> connected to M = 15 random neurons, so that the probability of connection is M / N = 0.15. It is<br> noticeable that in this network, not only the connection between two neurons is random, but also the<br> neurons type selection is random, too.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 13. Average fitness at generation progressing
<p>Figure 13 compares the average fitness at generations in one typical program running. As can be seen from this figure, the average of fitness grows as the number of generations increases.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 7. Effect of different type of movement on the image in the vision of artificial creature.
<p>Figure 7a, Figure 7b, Figure 7c and Figure 7d, shows effect of different type of movement on the image in the vision of artificial creature if food be on vision boundaries. As mentioned each part of the image equal 7.5 degree.<br> Therefore 15 degree left or right rotation locomotion equivalent two parts shift toward left or right.<br> For motion to forward direction, size of the image has been reduplicated so that each part has been become to the two similar parts. Then half of new image in right side and left side has been deleted in order to create new close image in vision. Accordingly, if food be on vision boundaries, the number of black parts of the image for the food object in ultimate location is 2, by one movement to forward direction the number of these parts become to 4, by one movement to forward direction the number of these parts become to 8 and so on. After four movements to forward all part of the image is black and the creature is succeed find the food object.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 6, One image in sight of artificial creature.
<p>Figure 6 shows an image in sight of the creature, where the food in this image is the dark part of the vision. If the food object places in the vision edge of the artificial creature (2 meters), two squares in the image in sight of the creature becomes black and by getting the artificial creature closer to the food, more squares of image in sight become block and if all parts of the image in sight of the creature become black, the artificial creature has been successful in finding the food object. In Figure 6 each part of the image divided to 3 subparts because 3 neurons per part of image in the input layer of the artificial creature network (vision) have been considered. So vision of the artificial creature composed of 60 neurons due to:</p> <p>For each black part of image in sight of the artificial creature, three signals as the input signals is applied to the three respective neurons.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 9. A typical artificial life form and circular area around of it
<p>As mentioned the reservoir neural network has been chosen for neural network of the artificial creature as main body structure. Figure 9 illustrates a typical artificial life form and circular area around of it. Maximum seeing of the artificial life form is periphery of the circular area and can't see places that have beyond of periphery of the circular area. The artificial creature by each movement is the center of a circular area so if the creature move to forward direct can see new<br> places and some places are voided of seeing.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 12. Genetic Algorithm flowchart
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cutpoints and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
BRAIN Journal-Artificial Neuron Modelling Based on Wave Shape-Figure 1. Typical feedforward artificial neural network processing unit
<p>In Figure 1, each input X is weighted by a separate weight value w. These are then summed together to give a total input value for the neuron. This total can then be passed through a function, to transform it into the desired output value. This is then compared to the actual output value d; where the error or differences between the two sets is measured and used to correct the weight<br> values, to bring the two sets of values closer. One way to update the weights is after each individual pattern is presented and processed. Another option is to process the error after the presentation of the whole dataset, as part of a batch update.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 4: Pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons
<p>In each time step, axonal conduction delays between presynaptic neurons and postsynaptic neurons are examined whether they are equal to the elements of array I_S, to apply the respective spikes.<br> The pseudo code for applying different axonal conduction delay between presynaptic neurons and postsynaptic neurons are shown in Figure 4.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 15. Movements a typical successful creature to find food
<p>Figure 15 (a, b, c, d, e, f, g, h) illustrates movements of a typical successful creature for finding one food object.</p> <p>We believe that this study can be a step forward in understanding the morphology of artificial creatures. Also this paper suggests more complex artificial life examination by adding different part to these networks similar to different segments of the brain such as: vision, locomotion, hippocampus and communication in a future work. Because of the different axonal conduction delay between every two neurons in the neural network of artificial creatures in this paper, our next study is to enhance the artificial lives by STDP learning.</p>
Figure 9. Classification accuracy regardless the ethnic group (Total accuracy 75%)-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Our experiments show that, the impact of ethnic group on the accuracy of emotions<br> recognition is a positive where the accuracy of emotion recognition considering ethnic group is<br> 83.3% as shown in Figure 8, and we got 75% of accuracy regardless ethnic group as shown in<br> Figure 9.</p>
Figure 8. Classification accuracy of emotions considering the ethnic group-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>To study the accuracy of emotion recognition for our approach regardless the ethnic group<br> we used 108 images for the training representing six emotions of six persons. For testing, we used<br> 36 images representing six emotions of six persons.<br> On the other hand, to study the accuracy of emotion recognition for our approach<br> considering the ethnic group we used 36 images for training for each ethnic group representing six<br> emotions of six persons, and test the classifier by using 12 images representing six emotions of six<br> persons.</p>
3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties
<p>Dataset for the article entitled "3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties", by Eva Baur, Benjamin Tiberghien and Esther Amstad published in Advanced Materials.</p>
Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"
<div> <div>Dataset accompanying the paper "<em>Adaptive connectivity control in networked multi-agent systems: A distributed approach</em>" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> <p>Detailed information and instructions are available in the README file within the dataset.</p> </div> </div> </div>
Structure and dynamics of enterovirus genotype networks
<p>Like all biological populations, viral populations exist as networks of genotypes connected through mutation. Mapping the topology of these networks and quantifying population dynamics across them is crucial to understanding how populations adapt to changes in their selective environment. The influence of mutational networks is especially profound in viral populations which rapidly explore their mutational neighborhoods via high mutation rates. Using a novel single-cell sequencing method, scRNAseq-Enabled Acquisition of mRNA and Consensus Haplotypes Linking Individual Genotypes and Host Transcriptomes (SEARCHLIGHT), we captured and assembled viral haplotypes from hundreds of individual infected cells to reveal the complexity of viral populations. We obtained these genotypes in parallel with host cell transcriptome information, enabling us to link host cell transcriptional phenotypes to the genetic structures underlying virus adaptation. Our examination of these structures reveals the common evolutionary dynamics of enterovirus populations and illustrates how viral populations reach through mutational 'tunnels' to span evolutionary landscapes and maintain connection with multiple adaptive genotypes simultaneously.</p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Train and test datasets used for the paper "Neural network time-series classifiers for gravitational-wave searches in single-detector periods"
<p>This repository contains the datasets used for training and testing during the work discussed in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad40f0" target="_blank" rel="noopener">Neural network time-series classifiers for gravitational-wave searches in single-detector periods</a>". Please refer to this paper for more details on how the dataset was produced and cite it if you use these data:</p> <p><em>A. Trovato et al "Neural network time-series classifiers for gravitational-wave searches in single-detector periods", Class. Quant. Grav. 2024 DOI 10.1088/1361-6382/ad40f0.</em></p> <p>In this repository you will find six files in format npz, three of which refer to the test dataset and three to the train dataset. Each file name is of the type {label}_{train or test}.npz where "label" can be "glitch", "noise" or "signal", while the second part of the name indicates whether the file was used for training or testing.</p> <p>Each file is a collection of numpy arrays so it should be read with python. It contains 3 numpy arrays: 'X', 'Y' and 'metadata'. 'X' is a matrix containing 1-second segments of data sampled at 2048 Hz of the LIGO-Livingston detector, so it has shape: (number of samples, 2048). 'Y' contains the label for each segment, which is 0 for noise, 1 for signal and 2 for glitch, so it has shape: (number of samples,). In this case, the information on 'Y' is redundant since it's given directly by the filename. The 'metadata' matrix contains 17 metadata for each sample only for the case of signals, for glitch or noise it contains just 17 zeros for each sample. The shape of 'metadata' is thus: (number of samples, 17). For the signal files, for each sample the metadata is an array with these components:</p> <ol> <li>GPS start of the file from which this segment comes</li> <li>starting GPS time of this segment</li> <li>duration of the segment [s]</li> <li>mass1 [solar masses]</li> <li>mass2 [solar masses]</li> <li>spin1z</li> <li>spin2z</li> <li>inclination [radians]</li> <li>coalescence phase [radians]</li> <li>distance [Mpc]</li> <li>right_ascension [radians]</li> <li>declination [radians]</li> <li>polarization [radians]</li> <li>SNR (signal to noise ratio)</li> <li>shift of the signal w.r.t. the timeseries [s]</li> <li>length of the signal [s]</li> <li>fraction of the signal contained in the time window</li> </ol> <p>Number of samples:</p> <ul> <li>80000 for the file glitch_test.npz</li> <li>69998 for the file glitch_train.npz</li> <li>500000 for the file noise_test.npz</li> <li>250000 for the file noise_train.npz</li> <li>500000 for the file signal_test.npz</li> <li>250000 for the file signal_train.npz</li> </ul> <p>An example of few lines of python code to read each file is:</p> <pre><code>import numpy as np f = np.load("filename.npz") X = f['X'] Y = f['Y'] m = f['metadata'] </code></pre> <p>For the preparation of these data, we acknowledge the use of the following software packages: GWpy [1], PyCBC [2] and LALSuite [3]. </p> <p>This research has made use of data or software obtained from the Gravitational Wave Open Science Center (<a href="https://gwosc.org/" target="_blank" rel="noopener">gwosc.org</a>), a service of the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the National Science Foundation, as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. KAGRA is supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan Society for the Promotion of Science (JSPS) in Japan; National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea; Academia Sinica (AS) and National Science and Technology Council (NSTC) in Taiwan.</p> <p>[1] https://gwpy.github.io<br>[2] https://pycbc.org<br>[3] https://lscsoft.docs.ligo.org/lalsuite</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.