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9,152 results for “Networking”
NsEllipse datasets from "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Datasets with simulations of the incompressible flow around an elliptical cylinder as described by the incompressible Navier-Stokes equations.</p> <p>These simulations were used to train and test the MuS-GNN models in the paper:<br> "Multi-scale rotation-equivariant graph neural networks for<br> unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679)</p> <p>The datasets are:<br> - train/NsEllipse<br> - test/NsEllipseLowRe<br> - test/NsEllipseHighRe<br> - test/NsEllipseThin<br> - test/NsEllipseThick<br> - test/NsEllipseNarrow<br> - test/NsEllipseWide<br> - test/NsEllipseAoA</p> <p> </p> <p>To cite these datasets, use the following reference:</p> <p>Mario Lino, Stathi Fotiadis, Anil A. Bharath, and Chris Cantwell. "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics". Physics of Fluids, 34 (2022).</p> <p>@article{lino2022multi,<br> author = {Lino, Mario and Fotiadis, Stathi and Bharath, Anil A. and Cantwell, Chris},<br> title = {{Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics}},<br> journal = {Physics of Fluids},<br> volume = {34},<br> year = {2022},<br> url = {https://doi.org/10.1063/5.0097679},<br> }<br> </p>
List of multinational publishers of serial publications established by the ISSN Network (PDF format)
<p>The ISSN International Centre and the ISSN Network of nearly 100 national centres around the world are authorized by the International Organization for Standardization (ISO) to implement serial identification according to the terms of ISO3297:2022 standard.<br> The ISSN Network serves publishers who wish to uniquely identify their serial publications. These publishers contact the National Centres of the countries where they are located. When there is no national centre, they contact the ISSN International Centre.<br> The place of publication determines the ISSN Centre responsible for identification. However, when a publisher operates in several countries, an agreement may be reached between this multinational publisher and the involved ISSN Centres so that all requests for identification are handled by one centre.<br> The following list identifies more than 300 multinational publishers. These publishers have been identified through the ISSN Portal (https://portal.issn.org) data. The ISSN International Centre works with the National Centres on this list of publishers to determine their responsibility for identifying the publications of these publishers.<br> The list also contains information on the names by which these publishers are known and a mapping of links between publishers. It also contains a link to the ISNI and VIAF databases in which these publishers are also recorded. This data is managed by the ISSN Network in an internal wiki that will be updated regularly.<br> The ISSN Network makes this list available to the publishing community and to librarians. They can contact the ISSN International Centre to share comments (additions, modifications, deletions) on this list which will be published quarterly.</p>
Social network inheritance and differentiation in wild baboons
<p>Immatures' social development may be fundamental to understanding important biological processes, such as social information transmission through groups, that can vary with age and sex. Our aim was to determine how social networks change with age and differ between sexes in wild immature baboons, group-living primates that readily learn socially. Our results show that immature baboons inherited their mothers' networks and differentiated from them as they aged, increasing their association with partners of similar age and the same sex. Males were less bonded to their matriline and became more peripheral with age compared to females. Our results may pave the way to further studies testing a new hypothetical framework: in female-philopatric societies, social information transmission may be constrained at the matrilineal level by age- and sex-driven social clustering.</p>
Photonic amorphous I-WP networks create angle-independent colors in Sternotomis virescens longhorn beetles
<p>Datasets supporting the manuscript "Photonic amorphous I-WP-like networks create angle-independent colors in <em>Sternotomis virescens</em> longhorn beetles" (DOI: 10.1002/adfm.202302720).</p> <p>Datasets are named according to the corresponding figures and contain raw data, with data related to each panel located in sub-folders named according to the panel. Further information about the data is found in README files for the entire dataset and each folder.</p>
Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize
<p>Relevant Data and Code for <em>Exploring the utility of regulatory network-based machine learning for gene expression prediction in maize </em>by Taylor Ferebee and Edward Buckler.</p> <p><strong>Input Data</strong></p> <p>The inputs of the models are enclosed in <em>Input_data-2022-001.zip</em></p> <p><strong>Output Data</strong></p> <p>The outputs of the models are enclosed in <em>Output_Results-2022-001.zip</em></p> <p><strong>Relevant Code </strong></p> <p>The code for all analyses is enclosed in <em>Code_Archive.zip </em></p>
Figure 4. Haplotype network for Rattus rattus Complex II in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 4. Haplotype network for Rattus rattus Complex II. The Nusa Tenggara samples are illustrated on the right of the network.
Figure 3. Haplotype networks for Rattus exulans, R in Expanding Population Edge Craniometrics and Genetics Provide Insights into Dispersal of Commensal Rats through Nusa Tenggara, Indonesia
Figure 3. Haplotype networks for Rattus exulans, R. argentiventer, and Rattus rattus Complex LIV. Sunda refers to the islands of Borneo, Java, and Sumatra; the Indonesian sample (brown) lacks further collection information.
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
Bone Age Assessment from Articular Surface and Epiphysis using Deep Neural Networks
<p>This is our research dataset of "Bone Age Assessment from Articular Surface and Epiphysis in Hand Radiography using Deep Neural Networks"</p>
Supplementary material for: PhyloCoalSimulations: A simulator for network multispecies coalescent models, including a new extension for the inheritance of gene flow
<p>We consider the evolution of phylogenetic gene trees along phylogenetic species networks, according to the network multispecies coalescent process, and introduce a new network coalescent model with correlated inheritance of gene flow. This model generalizes two traditional versions of the network coalescent: with independent or common inheritance. At each reticulation, multiple lineages of a given locus are inherited from parental populations chosen at random, either independently across lineages, or with positive correlation according to a Dirichlet process. This process may account for locus-specific probabilities of inheritance, for example.</p> <p>We implemented the simulation of gene trees under these network coalescent models in the Julia package PhyloCoalSimulations, which depends on PhyloNetworks and its powerful network manipulation tools. Input species phylogenies can be read in extended Newick format, either in numbers of generations or in coalescent units. Simulated gene trees can be written in Newick format, and in a way that preserves information about their embedding within the species network. This embedding can be used for downstream purposes, such as to simulate species-specific processes like rate variation across species, or for other scenarios as illustrated in this note. This package should be useful for simulation studies and simulation-based inference methods. The software is available open source with documentation and a tutorial at <a href="https://github.com/cecileane/PhyloCoalSimulations.jl">https://github.com/cecileane/PhyloCoalSimulations.jl</a>.</p>
DBsimilarity of Natural Products to aid compound identification on MS and NMR pipelines, similarity networking and more
<p>DBsimilarity is proposed for organizing structure databases into Similarity Networks to assist researchers with analyzing and making sense of chemical data available. It converts SDF files into CSV files when needed, adds chemoinformatics data, constructs a MZMine custom database file and a NMRfilter candidate list of compounds for rapid dereplication of MS and 2D NMR data, calculates similarities, and constructs CSV files for Similarity Networks using Cytoscape. DBsimilarity aims to bridge chemoinformatics to laboratory-focused natural products researchers and students. </p>
Deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT"- BN network and Bellman shortest path analysis_Module 3_Annex 6
<p>This file will introduce the BN network and Bellman shortest path analysis Module 3 Annex 6 in deliverable D6.2 "TOOL FOR PERFORMANCE ASSESSMENT".</p>
PIsToN: Evaluating Protein Binding Interfaces with Transformer Networks (dataset)
<p>Computational protein-binding studies are widely used to investigate fundamental biological processes and facilitate the development of modern drugs, vaccines, and therapeutics. Scoring functions aim to assess and rank the binding strength of the predicted protein complex. Accurate scoring of protein binding interfaces remains a challenge. PIsToN (evaluating Protein binding Interfaces with Transformer Networks) represents a novel approach to distinguish native-like protein complexes from incorrect conformations. Protein interfaces are transformed into a collection of 2D images (interface maps), each corresponding to a geometric or biochemical property. Pixel intensities represent the feature values. A neural network was adapted from a popular vision transformer (ViT) with several enhancements: a hybrid component to accept empirical-based energy terms, a multi-attention module to highlight essential features and binding sites, and the use of contrastive learning for better ranking performance. The resulting PIsToN model significantly outperforms state-of-the-art scoring functions on well-known datasets.</p> <p>This repository contains proteins and pre-computed interface maps for the PIsToN work.</p>
Mach-Zehnder-like interferometry with graphene nanoribbon networks
<p>S. Sanz, N. Papior, G. Giedke, D. Sanchez-Portal, M. Brandbyge, and T. Frederiksen<br><em>Mach–Zehnder-like interferometry with graphene nanoribbon networks</em><br><a href="https://iopscience.iop.org/article/10.1088/1361-648X/acd832">J. Phys.: Condens. Matter 35, 374001 (2023)</a></p> <p>We study theoretically electron interference in a Mach–Zehnder-like geometry formed by four zigzag graphene nanoribbons (ZGNRs) arranged in parallel pairs, one on top of the other, such that they form intersection angles of 60˚ Depending on the interribbon separation, each intersection can be tuned to act either as an electron beam splitter or as a mirror, enabling tuneable circuitry with interfering pathways. Based on the mean-field Hubbard model and Green's function techniques, we evaluate the electron transport properties of such 8-terminal devices and identify pairs of terminals that are subject to self-interference. We further show that the scattering matrix formalism in the approximation of independent scattering at the four individual junctions provides accurate results as compared with the Green's function description, allowing for a simple interpretation of the interference process between two dominant pathways. This enables us to characterize the device sensitivity to phase shifts from an external magnetic flux according to the Aharonov–Bohm effect as well as from small geometric variations in the two path lengths. The proposed devices could find applications as magnetic field sensors and as detectors of phase shifts induced by local scatterers on the different segments, such as adsorbates, impurities or defects. The setup could also be used to create and study quantum entanglement.</p>
Preprocessed datasets for experiments in the paper "Constrained Monotonic Neural Networks"
<p><strong>Preprocessed datasets used in the experiments of the paper:</strong></p> <ul> <li>Davor Runje, Sharath M. Shankaranarayana. <em>Constrained Monotonic Neural Networks</em>. International Conference on Machine Learning, 2023. [<a href="https://arxiv.org/pdf/2205.11775.pdf">pdf</a>]</li> </ul> <p>The code that runs the experiments can be found at:</p> <p><a href="https://github.com/airtai/monotonic-dense-layer">https://github.com/airtai/monotonic-dense-layer</a></p> <p> </p>
Interpretation of EKG with Image Recognition and Convolutional Neural Networks
<p>Dataset used in the training and evaluation of "Interpretation of EKG with Image Recognition and Convolutional Neural Networks"</p> <p>Abstract:</p> <p>Electrocardiograms (EKG) form the backbone of all cardiovascular diagnosis, treatment and follow up. Given the pivotal role it plays in modern medicine, there have been multiple efforts to computerize the EKG interpretation with algorithms to improve efficiency and accuracy. Unfortunately, many of these algorithms are machine specific and run-on proprietary signals generated by that machine, hence not generalizable. We propose the development of an image recognition model which can be used to read standard EKG strips. A convolutional neural network (CNN) was trained to classify 12-lead EKGs between 7 clinically important diagnostic classes. An austere variation of the MobileNetV3 model was trained from the ground up on publicly available labeled training set. The precision per class varies from 52% to 91%. This is a novel approach to EKG interpretation as an image recognition problem.</p>
The Network of Correspondents of Jobst Hermann von Ilten (1649-1730)
<p>The dataset contains graph data for the correspondence of the Hanoverian diplomat and politican Jobst Hermann von Ilten (1649-1730) in Gephi Project File format and JSON format. The data is based on von Ilten's collection of letters, which is part of the papers of his son Thomas Eberhard von Ilten (1685-1758). The original papers are held, digitized and catalogued at the Gottfried Wilhelm Leibniz Bibliothek in Hanover (GWLB).</p> <p>The Graph is designed to analyze the network of Jobst Hermann von Ilten and how it changed over time and identify key correspondents. For these purposes only correspondents with ten or more letters were considered. An exceptions with viewer letters were individuals of particular importance to Jobst Hermann von Ilten: his brother-in-law Otto XIII. Grote (1636-1693), the Prussian prime minister Eberhard von Danckelmann (1643-1722) and the Hanoverian General Carl Christian von Sommerfeldt (1650-1712).</p> <p>The Graph was created with Gephi 0.10 using the Fruchterman Reingold algorithm. For nodes intervals were defined based on the year of birth and the year of death of the person they represent. The intervals for the edges representing the correspondence are defined by the first and the last letter from each letter-writer. Intervals for the nodes in the Gephi Project File have not been exported to the JSON file. Intervals for the edges are included in both the Gephi Project File and the JSON file. </p>
Biochemical networks with simulation-based estimations of dynamical properties
<p>This datasets collection was first introduced in the article: </p> <p><a href="https://academic.oup.com/bioinformatics/article/39/11/btad678/7407341" target="_blank" rel="noopener">Exploiting the structure of biochemical pathways to investigate dynamical properties with neural networks for graphs</a>. (Bioinformatics 2023)</p> <p> </p> <p>The collection contains three datasets that contain information about three dynamical properties computed on a set of 483 biochemical pathways downloaded from the BioModels database. The three dynamical properties are:</p> <ul> <li>robustness</li> <li>sensitivity</li> <li>monotonicity</li> </ul> <p>The files are organized as follows:</p> <ol> <li>The `pathways` directory contains 483 files in .dot format for each biochemical pathway downloaded from the BioModels database (May 2021), represented in Petri net format (see <a href="https://doi.org/10.5220/0008964700320043">this article</a> for the exact definition). The file name is the ID of the pathway in the BioModels database.</li> <li>The other folders contain one .csv file for each property. A single .csv file contains 4 columns: <ol> <li>`PathwayID`: the ID of the Pathway in the BioModels database</li> <li>`Input`: the input molecular species on which the property has been assessed</li> <li>`Output`: the output molecular species on which the property has been assessed</li> <li>`Property`: the value of the property assessed with numerical simulations on the pathway for that particular input/output species pair.</li> </ol> </li> <li>The `loader.py` file is an optional script that allows to use the data in python. The script requires that the libraries `networkx`, `pandas`, and `pydot` are installed in the target machine.</li> </ol>
Supporting data for "Reliable interpretability of biology-inspired deep neural networks"
<p><strong>Contents</strong></p> <p><em>data.tgz</em> contains all data necessary for reproducing the analysis in the manuscript. After cloning the GitHub repository, extract the contents of this file into folder <em>data</em>. The archive contains the following subfolders:</p> <ul> <li><em>dtox</em><br> DTox results, one subfolder per seed <ul> <li><em>module_relevance.tsv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): compound identifier</li> <li>remaining columns: node identifiers (UniProt and Reactome IDs)</li> </ul> </li> <li><em>test_labels.csv</em>: predictions for the test set, with two columns: <ul> <li>truth: true label (0 or 1)</li> <li>predicted: predicted label (decimal number between 0 and 1)<br> </li> </ul> </li> </ul> </li> <li><em>mskimpact_[cancer type]_[experiment]</em><br> P-NET results using the MSK-IMPACT 2017 dataset, one subfolder per seed<br> [cancer type] is one of bc (breast cancer), cc (colorectal cancer), nsclc (non-small cell lung cancer), or pc (prostate cancer)<br> [experiment] is one of original (original setup) and shuffled (shuffled labels)<br> </li> <li><em>pnet_[experiment]</em><br> P-NET results using the original (prostate cancer) dataset, one subfolder per seed<br> [experiment] is one of deterministic (deterministic input data), original (original setup), and shuffled (shuffled labels) <ul> <li><em>node_importance.csv</em>: contains node importance scores, with the following columns: <ul> <li>(first, unnamed): node name</li> <li>coef: original node importance scores</li> <li>coef_graph: indegree plus outdegree of node</li> <li>coef_combined: adjusted node importance score (= coef / coef_graph if coef_graph > mean(coef_graph) + 5 sd(coef_graph) in the respective layer)</li> <li>coef_combined_zscore: scaled coef_combined</li> <li>coef_combined2: z(z(coef_graph) - z(coef))</li> <li>layer: layer of the node</li> </ul> </li> <li><em>predictions_test.csv</em>: predictions for the test set, with the following columns: <ul> <li>(first, unnamed): sample name</li> <li>pred: predicted class (unfortunately, encoded by a double 1.0 or 0.0)</li> <li>pred_scores: probability of the predicted class</li> <li>y: true class (encoded as integer 1 or 0)</li> </ul> </li> <li><em>predictions_train.csv</em>: predictions for the training set (same columns as above)</li> <li><em>link_weights_[layer].csv</em>: only in subfolder 234_20080808; matrices with edge weights</li> </ul> </li> </ul> <p> </p> <p><strong>Changelog</strong></p> <p><em>v1.1.0 – 2023-06-28</em></p> <ul> <li>added DTox results</li> <li>added results of P-NET experiments with MSK-IMPACT 2017 dataset</li> </ul> <p><em>v1.0.0 – 2023-03-22</em></p> <ul> <li>initial release</li> </ul>
Carbon configurations dataset generated in "A systematic approach to generating accurate neural network potentials: the case of carbon"
<p>This dataset contains 60133 configurations of Carbon as generated in the paper "A systematic approach to generating accurate neural network potentials: the case of carbon". The configurations represent crystal structures containing only Carbon atoms, ranging from 16 to 200 atoms in the unit cell, with energy and forces calculated through density functional theory. Please refer to the original paper for more details about the generation procedure and simulation parameters.</p> <p>The dataset consists of a compressed archive containing a single .xyz file with all configurations, with lattice and energy information contained in the comment line, and one line per atom with positions and forces. Lengths are in Angstroms and energies in eV.</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.