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9,152 results for “Networking”
Sub-pixel accuracy in electron detection using a convolutional neural network
<p><strong>Abstract</strong></p> <p>Modern direct electron detectors (DEDs) provided a giant leap in the use of cryogenic electron microscopy (cryo-EM) to study the structures of macromolecules and complexes thereof. However, the currently available commercial DEDs, all based on the monolithic active pixel sensor, still require relative long exposure times and their best results have been obtained at 300 keV. There is a need for pixelated electron counting detectors that can be operated at a broader range of energies, at higher throughput and higher dynamic range. Hybrid Pixel Detectors (HPDs) of the Medipix family were reported to be unsuitable for cryo-EM at energies above 80 keV as those electrons would affect too many pixels. Here we show that the Timepix3, part of the Medipix family, can be used for cryo-EM applications at higher energies. We tested Timepix3 detectors on a 200 keV FEI Tecnai Arctica microscope and a 300 keV FEI Tecnai G2 Polara microscope. A correction method was developed to correct for per-pixel differences in output. Timepix3 data were simulated for individual electron events using the package Geant4Medipix. Global statistical characteristics of the simulated detector response were in good agreement with experimental results. A convolutional neural network (CNN) was trained using the simulated data to predict the incident position of the electron within a pixel cluster. After training, the CNN predicted, on average, .39 pixel and 0.42 pixel from the incident electron position for 200 keV and 300 keV electrons respectively. The CNN improved the MTF of experimental data at half Nyquist from 0.39 to 0.70 at 200 keV, and from 0.06 to 0.65 at 300 keV respectively. We illustrate that the useful dose-lifetime of a protein can be measured within a 1 second exposure using Timepix3.</p> <p><strong>Data description</strong></p> <p>Data has been split up in experimental data, simulations, neural net models and ToT correction results. In general: each directory contains individual READMEs with steps how to reproduce the data.</p> <p><strong>Experimental data</strong></p> <p>For each type of data at 200 kV or 300 kV only the input raw data has been added and the resulting image file. Intermediate files have been left out.</p> <p><strong>Models</strong></p> <p>The models are the CNN models generated at 200 and 300 kV.</p> <p><strong>Simulated data</strong></p> <p>The simulated data consists of the dataset used for training the neural network and the indepedently simulated validation set. </p> <p><strong>ToT correction</strong></p> <p>The ToT correction file only contain the resulting correction matrix. The experimental flat field data has been left out, due to its volume. It's about 300 GiB of data for both 200 and 300 kV.</p> <p><strong>Software </strong></p> <p>The software used can be found as related identifiers to this deposit.</p>
The (Drawn) Distributed Network Around Polarstern, MOSAiC Expedition
<p>Digital drawings of the sensors and instruments that have been deployed on the sea ice as part of the "Distributed Network" around Polarstern - during leg 1 of the year-long MOSAiC expedition to the Arctic Ocean. The drawings can be freely used in presentations and for science communication, e.g. if a wide audience needs to be addressed. We would appreciate a proper citation (e.g. Krueger and Rackow, 2020). The drawings have all been done on board of the supporting Russian research vessel Akademik Fedorov in the Arctic Ocean.</p> <p>If you are interested in higher-resolution versions (or the individual layers), or if you want a particular instrument to be drawn as well, you can contact thomas.rackow@awi.de</p>
Input data files for RSS-NET analysis of IBD GWAS summary statistics and NK cell regulatory network
<p>Details of these data files are provided in https://suwonglab.github.io/rss-net/ibd2015_nkcell.</p> <p>Contact:<code> xiangzhu[at]psu.edu </code></p>
Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction
<p>We include samples of various data types used in the work "Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction"</p> <p>More information about the software package and instructions are provided in <a href="https://github.com/hallamlab/pathway2vec">hallamlab/pathway2vec</a></p>
Dataset for Neural Network 3D Body Pose Tracking and Prediction for Motion-to-Photon Latency Compensation in Distributed Virtual Reality
<p>Distributed Virtual Reality (DVR) systems enable geographically dispersed users to interact in a shared virtual environment. The realism of the interaction is crucial to increase the feeling of co-presence. Latency, produced either by hard- or software components of DVR applications, impedes reaching high realism levels of the DVR experience. For example, the time delay between the user's motion and the corresponding display rendering of the DVR system might lead to adverse effects such as a reduced sense of presence or motion sickness. One way of minimizing the latency is to predict user's motion and thus compensate for the inherent latency in the system. In order to address this problem, we propose a neural network 3D pose tracking and prediction system with latency guarantees for end-to-end avatar reconstruction. We evaluate and compare our system against multiple traditional methods and provide a thorough analysis on real-world human motion data. Datasets used in the paper experiments. Datasets used in paper experiments.</p>
The "Last.fm" data set used in the article "Cumulative effects of triadic closure and homophily in social networks"
<p>This is the "Last.fm" network used in the article:</p> <p>A. Asikainen, G. Iñiguez, J. Ureña-Carrión, K. Kaski, M. Kivelä. Cumulative effects of triadic closure and homophily in social networks. Science Advances (in press)</p> <p>https://doi.org/10.1126/sciadv.aax7310</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>The original data in which this network is based on was donwloaded from audioscrobbler.net where it was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license.</p> <p>The data contains two files:</p> <p><strong>lastfm.edg</strong><br> This is the network formatted as an edge list, where each row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p><strong>lastfm_genders.txt</strong><br> This is the list of genders of the nodes. Each row corresponds to one node. The first number is the node id (matching the one in the edge list) and the second number indicates the gender such that 0=male and 1=female.</p>
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
<p>This repository contains input files from the synthetic, curated, and processed experimental single-cell gene expression datasets used in BEELINE.</p> <p>New in version 3:<br> 1) Ground-truth networks used for analysis of experimental scRNA-seq datasets for mouse and human datasets<br> 2) Changed license to CC BY-NC 4.0 from GPL v3.0 to account for the non-commercial clause for the network data</p>
Network-based Drug Repurposing for Human Coronavirus
<p>Datasets from a study in which network-based methodologies were used to identify potential repurposable drugs and drug combinations for successfully targeting the 2019 novel coronavirus.</p>
Fig. 3 in Predicting the global mammalian viral sharing network using phylogeography
Fig. 3 Taxonomic and geographic patterns of mean predicted viral sharing link numbers (degree centrality). Top row: all viral sharing links; middle row: viral sharing links with species in the same order; bottom row: viral sharing links with species in another order. a, c, e Average species-level viral sharing link numbers for mammalian orders in our dataset. Bars represent means; error bars represent standard errors. b, d, f Geographic distributions of mean viral sharing link numbers. Distributions were derived by summing the viral sharing link numbers of all species inhabiting a 25 km2 grid square and dividing them by the number of species inhabiting the grid square, giving mean degree number at the grid level.
Fig. 1 Viral sharing GAMM outputs and data distribution. a in Predicting the global mammalian viral sharing network using phylogeography
Fig. 1 Viral sharing GAMM outputs and data distribution. a Predicted viral sharing probability increases with increasing phylogenetic relatedness; the different coloured lines represent different geographic overlap values. b Predicted viral sharing probability increases with increasing geographic overlap; the different coloured lines represent different phylogenetic relatedness values. c The geographic overlap:phylogenetic similarity interaction surface, where the darker colours represent increased probability of viral sharing. White contour lines denote 10% increments of sharing probability. Labels have been removed from some contours to avoid overplotting. d Hexagonal bin chart displaying the data distribution, which was highly aggregated at low values of phylogenetic similarity and especially of geographic overlap.
Data from: Assessing the effectiveness of a national protected area network in maintaining carnivore populations
<p>Protected areas (PAs) are essential to prevent further biodiversity loss yet their effectiveness varies largely with governance and external threats. Although methodological advances have permitted assessments of PA effectiveness in mitigating deforestation, we still lack similar studies for the impact of PAs on wildlife populations. Here we demonstrate the application ofuse an innovative combination of matching methods and hurdle-mixed models with a large-scale and long-term dataset of unprecedented coverage for Finland's large carnivore species. We show that the national PA network , at the national level, PAs does not support higher densities than non-protected habitat for 3 of the 4 species investigated. For the brown bear, PAs appear to have lower densities than non-protected areas. For some species, PA effects interact with region or time, i.e. wolverine densities decreased inside PAs over the study period and lynx densities increased inside eastern PAs. Although we show that matching approaches could and should be applied to wildlife population data, Wwe support their application of matching methods in combination of additional analytical frameworks for deeper understanding of conservation impacts on wildlife populations. These methodological advances are crucial for improving PA targets and extremely timely for preparing ambitious PA targets a post-2020 global framework for biodiversity.</p>
Data from: From structure to function in mutualistic interaction networks: topologically important frugivores have greater potential as seed dispersers
1. Networks of mutualistic interactions between animals and plants are considered a pivotal part of ecological communities. However, mutualistic networks are rarely studied from the perspective of species-specific roles, and it remains to be established whether those animal species more relevant for network structure also contribute more to the ecological functions derived from interactions. 2. Here, we relate the contribution to seed dispersal of vertebrate species with their topological role in frugivore-plant interaction networks. For one year in two localities with remnant patches of Colombian tropical dry forest, we sampled abundance, morphology, behavior, and fruit consumption from fleshy-fruited plants of various frugivore species. 3. We assessed the network topological role of each frugivore species by integrating their degree of generalization in interactions with plants with their contributions to network nestedness and modularity. We estimated the potential contribution of each frugivore species to community-wide seed dispersal, on the basis of a set of frugivore ecological, morphological and behavioral characteristics important for seed dispersal, together with frugivore abundance and frugivory degree. 4. The various frugivore species showed strong differences in their network structural roles, with generalist species contributing the most to network modularity and nestedness. Frugivores also showed strong variability in terms of potential contribution to seed dispersal, depending on the specific combinations of frugivore abundance, frugivory degree and the different traits and behaviors. 5. For both localities, the seed dispersal potential of a frugivore species responded positively to its contribution to network structure, evidencing that the most important frugivore species in the network topology were also those making the strongest contribution as seed dispersers. Contribution to network structure was correlated with frugivore abundance, diet, and behavioral characteristics. This suggests that the species-level link between structure and function is due to the fact that the occurrence of frugivore-plant interactions depends largely on the characteristics of the frugivore involved, which also condition its ultimate role in seed dispersal. 22-May-2020
Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
PyGNA: a unified framework for geneset network analysis
<p>Input data and results for the manuscript "PyGNA: a unified framework for geneset network analysis". </p> <p>Manifest files describe the content of each file. </p>
CellCognize: a neural network pipeline for cell type classification from flow cytometry data
<p>Readme file content</p> <p>The files stored here contain the following material as supplementary and source data for the publication</p> <p>Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data</p> <p>Birge D. Özel Duygan1, Noushin Hadadi1, Ambrin Farizah Bab1, Markus Seyfried2, Jan R. van der Meer1</p> <p>1 Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland<br> 2 Biotechnology Department, Firmenich SA, Geneva, Switzerland</p> <p>%%%%%%%<br> Flow cytometry data<br> %%%%%%</p> <p>FCM_files:</p> <p>.mat files with cleaned data as described in the supplementary methods section</p> <p>Ecoli_lakewater: raw FCM data (in .csv format) of E. coli cultures and E. coli cultures mixed to lakewater</p> <p>MIX_experiment_ACL_AJH_PVR: raw FCM data (in .csv format) of the synthetic three culture experiment with E. coli, A. johnsonii and P. veronii, as described in the main text and SI methods.</p> <p>PHE_OCT_enrichments: raw FCM data (in .csv format) of the phenol and 1-octanol enrichments and the 1-octanol isolates, as described in the main text and SI methods.</p> <p>%%%%%%%<br> Neural network data<br> %%%%%%</p> <p>NN_file_example: three ANN functions, to be used in conjunction with the SI methods section</p> <p>Supplementary_Methods.docx: Detailed description on the construction, usage and scripts for the ANN. To be used in conjunction with the Flow Cytometry data</p> <p>%%%%%%%<br> 16S sequencing data<br> %%%%%%</p> <p>raw fastq- files of the sample reads of the 1-octanol and phenol enrichments described in the paper, at t=0 and t=3d, each in triplicates, forward and reverse.</p> <p>Readme_16S_sequence_files.txt: sample description of the read files</p>
Four-area test network
<p>Test network consisting of four areas used in reliability studies at SINTEF Energy Research, see README.md for details.</p>
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://zenodo.org/record/3928550">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>If you use this dataset, please cite the original OpenSky paper:</p> <p>Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm.<br> "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research".<br> In<em> Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN)</em>, pages 83-94, April 2014.</p> <p>and the traffic library used to derive the data:</p> <p>Xavier Olive.<br> "traffic, a toolbox for processing and analysing air traffic data."<br> <em>Journal of Open Source Software</em> 4(39), July 2019.</p>
Datasets for reproduction of simulation study results presented in article "Network Aggregation to Enhance Results Derived from Multiple Analytics"
<p>The included datasets can be used to reproduce the simulation study results presented in the paper:<br> Duroux D., Climente-González H., Wienbrandt L., Van Steen K. (2020) Network Aggregation to Enhance Results Derived from Multiple Analytics. In: Maglogiannis I., Iliadis L., Pimenidis E. (eds) Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, vol 583. Springer, Cham.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 813533.</p>
Supplementary Materials (An Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population)
<p>Supporting information for an Artificial Neural Network Model for Assessing Frailty-Associated Factors in the Thai Population</p>
Classify on the Clock (CloCk) - An Entry Level Image Data Set for Neural Networks
<p><strong>General information</strong></p> <p>This data set contains synthetic images of an analog clock. Each time point from 00:00:00 - 11:59:59 is included as a separate image. We provide three different versions of each image with an increasing number of additional information:</p> <ul> <li><em>Sparse</em>: The image includes just the hour, minute and second hand</li> <li><em>Reduced</em>: Additional markings around the clock</li> <li><em>Full</em>: Additional written numbers</li> </ul> <p>The hands differ in size and color:</p> <ul> <li><em>Hour</em>: Black, short, wide</li> <li><em>Minute</em>: Blue, long, medium</li> <li><em>Second</em>: Red, long, slim</li> </ul> <p>We provide the following files in this data repository:</p> <ul> <li>RGB images with a size of 512x512 for all three versions</li> <li>A suggested training, validation and test split</li> <li>The creation script as Python file</li> </ul> <p>The script can easily be modified to create images with a different size and color.</p> <p> </p> <p><strong>Clock System</strong></p> <p>The movements of the hands follow a linear relationship. Their angles can be calculated by the forward system:</p> <p><span class="math-tex">\( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p>One can also introduce rotated versions of the images. The system becomes non-linear in this case:</p> <p><span class="math-tex">\(\operatorname{mod}\left( \begin{bmatrix} 6^\circ & 0 & 0 \\ 0.1^\circ & 6^\circ & 0 \\ 0 & 0.5^\circ & 30^\circ \end{bmatrix} \begin{pmatrix} n_{\text{sec}} \\ n_{\text{min}} \\ n_{\text{hour}} \end{pmatrix} + \omega, \, 360^\circ \right) = \begin{pmatrix} \alpha_{\text{sec}}\\ \alpha_{\text{min}} \\ \alpha_{\text{hour}} \end{pmatrix}.\)</span></p> <p> </p> <p><strong>Use Cases</strong></p> <p>This data set was originally designed for basic research on Capsule Networks. Use cases are:</p> <ul> <li>Evalutation of classification and regression performance</li> <li>Influence of image transformations, e.g. rotations</li> <li>Detection of the hierarchy & relationship of image parts</li> <li>Concealment of objects in the image</li> <li>Interpretability of the learned model</li> <li>Solving a discrete inverse problem (<em>sparse</em> version)</li> <li>...</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.