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9,152 results for “Networking”
Ephemeral stream water contributions to United States drainage networks
<p>Results from our CONUS ephemeral stream modeling study. Included in this release is a set of csv files for each CONUS drainage basin (USGS HUC level 4 basins). These lookup tables can be joined to NHD-HR hydrography (https://www.usgs.gov/national-hydrography/nhdplus-high-resolution). CSV columns are as follows:</p> <ul> <li>NHDPlusID: NHD-HR reach ID. Use this to join to hydrography shapefiles.</li> <li>perenniality: Result of our ephemeral classification model (see paper for methods).</li> <li>percQEph_reach: Main model result. See equation S1 in paper.</li> </ul> <p> </p> <p>Some notes:</p> <ul> <li>Note that continental-scale models prioritize capturing broad patterns across large domains, so we caution against over-interpreting results for individual river reaches.</li> <li>Great Lakes are not included, though the model does route through the lakes for topological consistency.</li> <li>Foreign streams are recast as 'non-ephemeral' to be copacetic with figures in the paper. See paper for more details.</li> <li>Some reaches present in the NHD-HR are not included in our results for a variety of reasons. See paper for details.</li> </ul> <p> </p>
Datasets underlying manuscript "Earthquake observatory with coherent laser interferometry on the telecom fiber network"
<p>Refers to the paper: "Seismic monitoring using the telecom fiber network" by S. Donadello et al.., Communications Earth and Environment, 5,178 (2024). DOI: 10.1038/s43247-024-01338-2</p> <p>Data are structured in folders corresponding to manuscript figures 1 to 4 and Extended Figures S1 to S6.</p> <p>Headers unambiguously indicate the listed quantities.</p> <p>Minimal python scripts are provided to support interpretation and visualization</p>
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
<h1>Datasets and analysis code of the following publication:</h1> <p>Peng Liu, Ke Bo, Mingzhou Ding and Ruogu Fang (2024). Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. <em>PLOS Computational Biology. </em>DOI: 10.1371/journal.pcbi.1011943</p> <p>For any questions please contact the first author at mail pliu1 [at] ufl [dot] edu</p> <h2><strong>Contents:</strong></h2> <p><strong> Code_DataAnalysis</strong><br> - Extracted Selectivity</p> <p> -- IAPS and NAPS datasets</p> <p> -- Neurons In Alexnet and VGG networks</p> <p> --Networks are pre-trained on ImageNet and randomly initialized</p> <p> - Extracted Overlapped Selectivity across IAPS and NAPS.</p> <p> - Extracted tuning performance changes from two datasets and the VGG network</p> <p> -Code to replicate the key results including </p> <p> --Tuning quality </p> <p> -- Number of overlapped neurons</p> <p> -- Enhance neuron activity</p> <p> -- Lesion neurons</p> <p> <strong>TrainedNetworks</strong></p> <p> --Pre-trained VGG network on ImageNet </p> <p> --Pre-trained Alexnet network on ImageNet</p> <p>After pre-training these networks on ImageNet, we fixed their weights and trained them to classify pleasant, neutral, and unpleasant images into three emotion categories using both IAPS and NAPS datasets.<br> </p> <p><strong>Image datasets</strong></p> <p>Access image datasets by request from https://csea.phhp.ufl.edu/media/iapsmessage.html for IAPS and https://lobi.nencki.edu.pl/research/8/ for NAPS.</p>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Social network connections are positively related to temperature in winter flocks of black-capped chickadees
<p>Social species often share with conspecifics the responsibilities of finding food, defending against predators or caring for young. Within a social group, individuals' roles can be influenced by age, sex, and personality. Black-capped chickadees, <em>Poecile atricapillus</em>, are nonmigratory passerine birds that spend their winters living in social flocks. Due to the intense metabolic stress placed on chickadees by low winter temperatures, the role of flocking in resource localization and allocation among flockmates is an important aspect of individual fitness, especially during winter. Understanding changes in flock structure in response to the environment may provide insight into the consequences of social behaviour variability on overwinter survival. Here, we used social network analysis to quantify the effects of ambient temperature on social foraging behaviour in winter flocks of black-capped chickadees over two winters in Massachusetts, USA. Contrary to our expectations, we found that two measures of social connection were higher on warm days than on cold days. We found no evidence that sex or dominance rank influenced birds' behavioural responses to temperature. Furthermore, contrary to previous research, we found male-biased sex ratios in most of our flocks. Our findings that birds had more and stronger social connections on warm days and that an individual's response to temperature was not related to their status in a flock beg further investigation as to the specific costs and benefits of flocking in chickadees and other birds.</p>
Unsolved networks for use in pypsa-eur-ukraine
<p>9 node Ukrainian electricity network at 3 hour and 1 hour time resolutions, 1 year. </p>
Data set for Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks
<p>This document contains all the data and analysis used in the manuscript titled </p> <h1><span>Coherent Hole Transport in Selective Area Grown Ge Nanowire Networks</span></h1> <p><span><a title="DOI URL" href="https://doi.org/10.1021/acs.nanolett.2c00358">https://doi.org/10.1021/acs.nanolett.2c00358</a></span></p>
Learning Useful Representations of Recurrent Neural Network Weight Matrices
<p>Dataset of RNN weights for the ICML 2024 paper "Learning Useful Representations of Recurrent Neural Network Weight Matrices". See <a title="GitHub repository" href="https://github.com/vincentherrmann/rnn-weights-representation-learning">https://github.com/vincentherrmann/rnn-weights-representation-learning</a>.</p> <h1> </h1>
Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 in Transcriptomic analysis of wound-healing in Solanum tuberosum (potato) tubers: Evidence for a stepwise induction of suberin-associated genes
Fig. 3. Network modules for suberin-associated metabolism genes. Expression profiles for 317 wound-induced and suberin-associated genes encompassing primary carbohydrate metabolism and the formation of suberin phenolic and aliphatic monomers were subjected to WGCNA. Genes belonging to carbohydrate (C), tricarboxylic acid cycle (TCA), shikimate pathway (S), phenolic metabolism (P), phenolic assembly (PA), fatty acid biosynthesis (FAB), fatty acid modification (FAM) and aliphatic assembly (AA) are colour-coded (see legend). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Mombasa TEOM and Clarity network data
<p>data from TEOM and clarity sensors in Mombasa kenya. </p>
Superconducting string network
<p>Animations showing the evolution a superconducting string network in radiation epoch, from conformal time 192 to 255. Strings are colored by the phase of the condensate (to explicitely show current) and then isosurfaces of the condensate are overlayed in green.</p>
Simulation data for the manuscript "Characterizing Optimal Signal Propagation in the Human Brain Network."
<p>See <a href="https://github.com/kuffmode/OI-and-CMs">https://github.com/kuffmode/OI-and-CMs</a></p>
Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition"
<p>Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition".</p> <p>neutron_data.zip --> neutron data in .nxspe form. S(Q,E) calculated using the DAVE software. Includes logbook spreadsheet. </p> <p>Training_Data.xyz --> xyz file containing training data used to generate Allegro machine learning forcefield in the paper</p> <p>nh3_pimd.deploy --> Trained Allegro model to that can be used in LAMMPS and RXMD software a ML forcefield </p> <p>POSCAR_UNIT_CELL_AMMONIA --> NH3 unit cell in solid phase in POSCAR format that can be read by the VASP software used to perform the DFT simmulations.</p>
Pappulab/N130-Liquid-Structure: Biomolecular condensates form spatially inhomogeneous network fluids
<p>This repository contains the data for the Nature Communications manuscript, "Biomolecular condensates form spatially inhomogeneous network fluids" by F. Dar†, S. R. Cohen†, D. M. Mitrea, A. H. Phillips, G. Nagy, W. C. Leite, C. B. Stanley, J-M. Choi†,§, R. W. Kriwacki§, R. V. Pappu§. (†Equal contributors; §Co-corresponding authors). (2024). <a href="https://doi.org/10.1038/s41467-024-47602-z" target="_blank" rel="noopener">https://doi.org/10.1038/s41467-024-47602-z</a></p> <p>For accompanying analysis scripts, please consult: <a href="https://github.com/Pappulab/n130-liquid-structure" target="_blank" rel="noopener">https://github.com/Pappulab/n130-liquid-structure</a>.</p>
Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus
<p>Dataset accompanying the manuscript "Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus". This dataset is used to re-create all figure panels with underlying data in the manuscript.</p>
Stemless InSb nanowire networks and nanoflakes grown on InP
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Transposable element products, functions, and regulatory networks in Arabidopsis thaliana
<h1>README</h1> <p>This dataset includes the main outputs from the work titled <strong>Transposable element products, functions, and regulatory networks in <em>Arabidopsis thaliana</em>.</strong></p> <h2><strong>Summary</strong></h2> <p>Transposable elements (TEs) are DNA sequences with the ability to propagate themselves within and across genomes. Their mobilization is catalyzed by self-encoded factors, yet these factors have been poorly investigated due to difficulties in defining TE genes in genomes. Here, we leveraged extensive long- and short-read transcriptome data, together with structural predictions, transcription factor binding site identification, and transcriptional network analyses, to construct a comprehensive atlas of TE transcripts and TE-encoded products in the model organism <em>Arabidopsis thaliana</em>. We uncovered hundreds of transcriptionally competent TEs, each potentially encoding multiple proteins either through distinct genes, alternative splicing, or post-translational processing. Structural-based protein analyses revealed dozens of hitherto unidentified domains of unknown function, enabling us to predict proteins with multimerization and DNA binding domains forming macromolecular complexes involved in transposition. Furthermore, we demonstrate that TE expression is highly intertwined with the transcriptional network of cellular genes, and identified transcription factors and cis-regulatory elements associated with their coordinated expression during development or in response to environmental cues. This comprehensive atlas of TE-genes and TE-proteins provides a valuable resource for studying the mechanisms involved in transposition and their consequences for genome and organismal function.</p> <h2><strong>File description</strong></h2> <p>It includes the following data:</p> <ol> <li><code>annots/TE_Functional_Annotation.Borreda2024.gtf</code> - Annotation file including Arabidopsis TEs and TE-genes. TE-genes defined in our work are indicated in the 'Source' column of the gtf. TAIR10-defined TEs for whom we did not annotate new transcripts are also included.</li> <li><code>seqs</code> - This folders includes all the transcript sequences (cDNAs.tsv) and the first and longest ORFs found in each of them (prot.csv), which were used for further analyses. The specific copy, gene, isoform and, in the case of proteins, ORF, is indicated for each sequence.</li> <li><code>structures</code> - The zipped folder <code>full_length_prots_pdbs.zip</code> includes all the 3D structures from full-length TE proteins. Note that identical proteins, which would result in identical structures, have been collapsed to reduce the total dataset size; equivalences can be found in <code>identical_proteins</code>.</li> <li><code>structures/SD_Cluster_Functions.tsv</code> - We clustered all Structural Domains (SD) based on 3D similarity and assigned a function to each cluster based on the database hits. This table indicated, for each of these SDs, to which cluster it belongs, the superfamily, family and element containing it, the number of Conserved Domains included within it and the number of hits with resolved (retrieved from the RCSB-PDB database) or predicted (AlphaFold2) protein structures. The last column includes the putative function assigned to each cluster.</li> <li><code>coexpression</code> - Coexpressed genes were classiffied into modules using WGCNA. In the table <code>Gene_Modules.tsv</code> we include, for each gene and TE-gene (provided it has expression in at least one sample, see methods on the publication for details), the TE family and superfamily when applicable and the module to which it belongs. The modules were named based on the results of the GO enrichment analysis of the genes contained. The results of this GO enrichment are included in <code>GO_Enrichment.tsv</code>, where we include the main funciton of the associated GOs, the number of entries and TE-genes within the module, a list of GO terms enriched in that specific module and finally a list of TE families enriched in each module.</li> <li><code>dapseq</code> - We reanalized the DAP-seq dataset from O'Malley 2016, selecting only TFBS with a binding site within a DAP-seq peak. The list of filtered peaks we found is reported in <code>DAPseq_TFBS_Motifs.tsv</code>. The columns include the coordinates of the TFBS (which have been filtered to fall within a DAP-seq peak and include the TFBS motif), the strand of the motif, the score of the motif reported by FIMO, the motif sequence, the Sequence Read identified for the original DAP-seq data, and the family, name and gene of the TF associated with that specific peak.</li> </ol>
State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
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Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Figure for "A quick battery charging curve prediction by artificial neural network"
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ScienceDex guides
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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.