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9,152 results for “Networking”
Resting-state fMRI data for locating causal hubs of memory consolidation in spontaneous brain network
<p>The mouse fMRI data for the paper "<strong>Locating causal hubs of memory consolidation in spontaneous brain network in male mice</strong>"<strong> </strong>published in <strong>Nature Communications </strong>(DOI: 10.1038/s41467-023-41024-z)<strong>. </strong>This includes longitudinal resting-state fMRI data in mice after behavioural training for 1-Day or 5-Day Active Place Avoidance (APA) task, acquired at post-training day 1 and day 8. Due to the large datasets, each group has been packed into several 2GB zip files. They need to be downloaded into the same folder and unpacked together (e.g. 1-Day APA Post training day 1 has five zip files starting with "1DAPA_PostDay1"). The structural and EPI templates and the ROI labels in the AMBMC atlas space are provided in the AMBMC_label.zip. </p>
Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks
<p>This repository contains training, validation, and testing of fluorescence image data sets with their label for spheroid cell death classification. These data are intended to be used in the paper <strong>"Prediction of Spheroid Cell Death using Fluorescence Staining and Convolutional Neural Networks" currently submitted </strong></p>
VNMPF-LIS: Validation Network Multiplatform Precipitation Feature (VNMPF) Dataset with International Space Station Lightning Imaging Sensor (ISS LIS) Data
<p>The Multiplatform Precipitation Feature (MPF) database combines ground- and space-based precipitation observations and retrievals from the Global Precipitation Measurement (GPM) mission Validation Network (VN) with space-based lightning measurements from the Lightning Imaging Sensor on board the International Space Station (ISS LIS). The data are synthesized in a thunderstorm-like, feature-based framework that encapsulates the microphysical, kinematic, and electrical properties of the observed storm.<br> <br> A VNMPF includes:</p> <p>- Radar information, GPM orbit, and ISS orbit <br> - Time/date information<br> - Geographical information<br> - Radar reflectivity characteristics<br> - Lightning energetic and identification information (where there is lightning)<br> - 3-dimensional wind information (where radars in dual-Doppler configuration are available)<br> <br> Version 1: 2017-2020</p> <p>Version 2: 2017-2022, updated VN winds </p>
Photometric Completeness Modelled With Neural Networks
<p>Neural networks associated with the paper "Photometric Completeness Modelled With Neural Networks" (Harris & Speagle 2023).</p> <p>Neural networks (`nn_clf_[...].joblib`) are included for all possible parameter combinations and trained over various numbers of artificial star tests (`ngc[...].dat`). See the example notebook (`nn_example.ipynb`) for detailed explanations of the files, their contents, and some usage examples.</p>
RIDDLER: An updated astrochemical network for modeling TMC-1
<p>The input files in RIDDLER.zip are those used in Byrne et al. 2023 (in revision) for modeling the propargyl radical and related species in TMC-1. This network is based on the <a href="https://arxiv.org/abs/1503.01594">kida.uva.2014</a> network and intended to be used with the three-phase gas-grain code NAUTILUS. A number of updates have been made by the GOTHAM collaboration (<a href="https://arxiv.org/abs/1801.04228">McGuire et al. 2018</a>, <a href="https://arxiv.org/abs/2008.12345">Xue et al. 2020</a>, <a href="https://arxiv.org/abs/2008.12349">McGuire et al. 2020</a>, <a href="https://arxiv.org/abs/2009.13546">McCarthy et al. 2021</a>, <a href="https://arxiv.org/abs/2103.09984">McGuire et al. 2021</a>, <a href="https://www.nature.com/articles/s41550-020-01261-4">Loomis et al. 2021</a>) as well as the most recent update targeting propargyl radical chemistry. The files in GOTHAM-DR1.zip correspond to the previous GOTHAM astrochemical network, with which comparisons have been made.</p>
List of multinational publishers of serial publications established by the ISSN Network
<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> This 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>
ASN Database - v3.2 - Database of Simulated Room Impulse Responses for Acoustic Sensor Networks Deployed in Complex Multi-Source Acoustic Environments
<p>We present a large set of simulated room impulse responses for a multi-room apartment. The simulated apartment models a real vacation apartment for which a recorded set of audio data has already been made available in the context of the DCASE challenges. The impulse responses were rendered using a dense grid of sources and receivers by means of a hybrid auralization algorithm based on a low-order image-source method and deterministic cone tracing. The proposed data set can be used to generate a wide variety of acoustic scenes which, in turn, can benefit numerous data-demanding machine-learning algorithms.<br> <br> To obtain more information on the database, please visit <a href="https://github.com/Jearde/asn-database">the website</a>.<br> <strong>Please read the license file (available in the GitHub repository) before using the database.</strong></p>
Building a knowledge graph of the Benetton's group suppliers network
<p>Some resources used for the PW4 of the master's AI4GOV. It concerns the Benetton Group's supplier network</p>
Tissue-specific Regulatory Network from Badr's work
<p>Mapping perturbed molecular circuits that underlie complex diseases remains a great challenge. We developed a comprehensive resource of 394 cell type- and tissue-specific gene regulatory networks for human, each specifying the genome-wide connectivity among transcription factors, enhancers, promoters and genes. Integration with 37 genome-wide association studies (GWASs) showed that disease-associated genetic variants--including variants that do not reach genome-wide significance--often perturb regulatory modules that are highly specific to disease-relevant cell types or tissues. Our resource opens the door to systematic analysis of regulatory programs across hundreds of human cell types and tissues (http://regulatorycircuits.org).</p> <p>hs_blood_network.rds : human blood related gene regulatory networks (R program data file)</p> <p>hs_pantissue_network.rds : human pan-tissue related gene regulatory networks (R program data file)</p> <p>mm_blood_network.rds : mouse blood related gene regulatory networks (R program data file)</p> <p>mm_pantissue_network.rds : mouse pan-tissue related gene regulatory networks (R program data file)</p> <ul> <li>PMID: <strong>26950747</strong></li> <li>PMCID: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/pmc4967716/">PMC4967716</a></li> <li>DOI: <a href="https://doi.org/10.1038/nmeth.3799">10.1038/nmeth.3799</a></li> </ul>
Data from: Urbanization alters the spatiotemporal dynamics of plant-pollinator networks in a tropical megacity
<p><span>Urbanization is a major driver of biodiversity change but how it interacts with spatial and temporal gradients to influence the dynamics of plant-pollinator networks is poorly understood, especially in tropical urbanization hotspots. Here, we analyzed the drivers of environmental, spatial, and temporal turnover of plant-pollinator interactions (interaction β-diversity) along an urbanization gradient in Bengaluru, a South Indian megacity. The compositional turnover of plant-pollinator interactions differed more between seasons and with local urbanization intensity than with spatial distance, suggesting that seasonality and environmental filtering were more important than dispersal limitation for explaining plant-pollinator interaction β-diversity. Furthermore, urbanization amplified the seasonal dynamics of plant-pollinator interactions, with stronger temporal turnover in urban compared to rural sites, driven by greater turnover of native non-crop plant species (not managed by people). Our study demonstrates that environmental, spatial, and temporal gradients interact to shape the dynamics of plant-pollinator networks and urbanization can strongly amplify these dynamics. </span></p>
Data from: Species-habitat networks reveal conservation implications that other community analyses do not detect
<p><span>Grassland restoration is an important conservation intervention supporting declining insect pollinators, particularly in threatened calcareous grassland landscapes. While restoration is often assessed using simple diversity or the similarity to a target community metrics, this can fail to represent key aspects of community reconstruction. Here, we compare a new method, species-habitat networks, with techniques previously relied upon to understand the process of pollinator community restoration. The species-habitat network approach reveals details relevant to insect conservation that are not visible using standard measures of species richness, abundance, community similarity or network metrics. For instance, a shared set of butterflies and bumblebees found in ancient extensively managed grassland, the target community for restoration, were more likely to inhabit previously disturbed grassland than recently disturbed or reverting grasslands. We propose that species-habitat networks should be part of the standard analytical toolkit assessing the effectiveness of restoration, particularly for mobile species such as insects.</span></p>
Dataset supporting: Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks
<p>This dataset supports the Figures in the preprint: 'Repetitive transcranial magnetic stimulation (rTMS) triggers dose-dependent homeostatic rewiring in recurrent neuronal networks' by Anil et. al., <a href="https://www.biorxiv.org/content/10.1101/2023.03.20.533396v1">2023</a>.</p> <p>The code used to genrate this data can be found on the github repository: <a href="https://github.com/swathianil">swathianil </a><a href="https://github.com/swathianil/homeostatic_structural_plasticity_rTMS"> /homeostatic_structural_plasticity_rTMS</a>.</p> <p>To interactively regenerate the figures, follow the Jupyter notebook, GraphPlotter, available in the repository.</p>
Data from: Culling-induced perturbation of social networks of wild geese reinforces rather than disrupts associations among survivors
<p>Wildlife populations may be the subject of management interventions for disease control that can have unintended, counterproductive effects. Social structure exerts a strong influence over infectious disease transmission in addition to other characteristics of populations such as size and density that are the primary target for disease control. Social network approaches have been widely used to understand disease transmission in wildlife but rarely in the context of perturbations, such as culling, despite the likely impacts of such disturbance on social structure and disease dynamics. Here we present a 'removal' study of a free-living population of resident Canada geese <em>Branta canadensis</em>, a highly social species that is frequently managed by culling and can carry pathogens relevant to human and domestic animal health. We quantified social network structure and spatial behaviour before and after controlled culling of individuals during the summer moult. Culling did not substantially increase individual social connectivity. Individuals that moulted at cull sites or were formerly strongly associated with removed birds were more likely to strengthen and maintain any surviving existing associations while also forming new associations. However, the establishment of new associations was largely compensatory (with only small increases in the number and strength of connections) and occurred locally. Synthesis & applications: geese that survived the cull responded by strengthening existing social relationships and forming new, compensatory relationships with birds local to them in the network. In the short-term such compensatory adjustments to patterns of association in response to culling could facilitate pathogen transmission. But in the longer term, controlled culling of geese is unlikely to strongly influence pathogen spread and may even slow transmission into new social clusters by reducing wider mixing. When managing wildlife for disease control, in addition to changes in social network structure the prevalence of infection at the time of the cull and the mode of transmission (e.g., direct versus environmental) will also be critical determinants of disease transmission risk in perturbed populations of geese and other wild animals.</p>
Dataset: Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks
<p>Dataset for the paper E. Rinaldi, M. González Lastre, S. García Herreros, S. Ahmed, M. Khanahmadi, F. Nori, and C. Sánchez Muñoz (2023), <a href="https://arxiv.org/abs/2310.02309">¨Parameter estimation by learning quantum correlations in continuous photon-counting data using neural networks¨, arxiv: 2310.02309</a></p> <p>This dataset can be used to populate the [datapath] folder in the repository <strong>ParamEst-NN</strong> (<a href="https://github.com/CarlosSMWolff/ParamEst-NN">github.com/CarlosSMWolff/ParamEst-NN</a> ) and reproduce the results shown in the paper.</p> <p>The dataset consist of four folders:</p> <ol> <li><strong>Training trajectories.</strong> Records of quantum-jump trajectories simulated with the Monte-Carlo solver of the <a href="https://qutip.org/">QuTiP</a> library, used to train neural networks for the problem of quantum parameter estimation. The records consist of time delays between quantum jumps.</li> <li><strong>Models. </strong>Models trained with the training trajectories provided, and used to obtain the results shown in the paper.</li> <li><strong>Validation trajectories. </strong>Trajectories used to benchmark the trained models. For the 2D case, the same trajectories are provided as a single .npy file, and split in 10 separated batches inside a ```batches``` folder. These are the batches that we used to generate Bayesian estimations on a cluster using nested sampling (see README file of the <a href="http://github.com/CarlosSMWolff/ParamEst-NN">repository</a>).</li> <li><strong>Cached results. </strong>Here we provide pre-computed Bayesian estimations for the 2D multi-parameter estimation case using nested sampling.</li> </ol> <p> </p> <p><em>E.R. was supported by Nippon Telegraph and Telephone Corporation (NTT) Research during the early stages of this work.<br> C.S.M. acknowledges that the project that gave rise to these results received the support of a fellowship from “la Caixa” Foundation (ID 100010434) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No.847648, with fellowship code LCF/BQ/PI20/11760026, and financial support from the MCINN project PID2021-126964OB-I00 (QENIGMA) and the Proyecto Sinérgico CAM 2020 Y2020/TCS- 6545 (NanoQuCo-CM).</em></p>
From MS/MS Library Implementation to Molecular Networks: Exploring Oxylipin Diversity with NEO-MSMS library
<p>Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) database comprising pure NEO-PUFA (both commercial and synthesized) and<em> in vitro</em> free radical-induced oxidations of diverse PUFAs and oxylipins. Furthermore, we conducted various molecular networking analyses on these standards, as well as on in vitro free radical-induced oxidations, serum samples, and algae samples. This was done to demonstrate their potential in derreplication and to provide additional putative oxylipins This dataset contains all the data used in this study including the raw data and the MzMine 3 procesed data used to built the molecular networks (i.e. feature tables and MSMS spectra).</p> <p>The folder ‘libraries’ contained the NEO-MSMS library divided into three .mgf files, i.e., the standards obtained with Q-Exactive Focus (i.e. NEOMSMS_QFocus_lib.mgf), Orbitrap ID-X Tribrid (i.e. NEOMSMS_idX_lib.mgf), and from the Watrous et al. study (Cell Chem Biol., 2019; 26(3):433-442) (NEOMSMS_Watrous_lib.mgf). Moreover, the individual .mgf files from each compound was included in three subfolders following the same syntax. The folder ‘oxPUFAs’ contains the raw .mzML files of the LC-MSMS analysis and the mzMine alignment results (i.e., the feature tables and the extracted .mgf files) of the <em>in vitro</em> oxidized PUFAs. The folder ‘algae_serum’ contains the same type of data from the algae and serum samples. The folder ‘oxOxylipins’ contains the raw .mzML files of the LC-MSMS analysis of the miniaturized oxidation of HETE, rearrangement of PGH<sub>1</sub> and PGH<sub>2</sub>. Finally, the ‘putative_lib´ folder contains the putatively annotated oxylipins in the same structure as ‘libraries’ folder.</p>
UKESM1.0-ice simulation output used as test data in Burgard et al., Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks
<p>These files contain NEMO ocean model output and domain definitions for the Southern Ocean from UKESM1.0-ice simulations described in section 6.3.2 of Smith et al. "Coupling the U.K. Earth System Model to Dynamic Models of the Greenland and Antarctic Ice Sheets" , Journal of Advances in Modeling Earth Systems, 2021</p> <p>Files labelled "bf663" are the UKESM simulation referred to in that section as "constant 1970 greenhouse gas and other forcings". Files labelled bi646 are the UKESM simulation referred to in that section as "instantaneously quadrupled 1970 CO<sub>2</sub> concentrations".</p> <p>They were used as test data for the performance of neural networks in Burgard et al., "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks", Journal of Advances in Modeling Earth Systems 2023.</p>
Pretrained models and simulated data for MICCAI paper Unsupervised Domain Transfer with Conditional Invertible Neural Networks
<p>Simulated data and the pretrained models used for the publication "Unsupervised Domain Transfer with Conditional Invertible Neural Networks", see https://link.springer.com/chapter/10.1007/978-3-031-43907-0_73 published at MICCAI 2023.</p>
Dataset for Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks
<p>Data set for the publication named: "Validation of a Qualification Procedure Applied to the Verification of Partial Discharge Analysers Used for HVDC or HVAC Networks"</p>
Required data for simulating a typical large-scale urban traffic network
Open the record for dataset details and reuse information.
Structural Characterization of amphiphilic co-networks in selective and non-selective solvents using 1H-NMR and SAXS
<p>We investigate structural properties of model amphiphilic co-networks made by heterocomplementary end-linking of tetra-PEG tetra-PCL star polymers in selective and non-selective solvent using small-angle X-ray scattering, NMR diffusometry and double-quantum magic-angle spinning (DQ MAS) NMR techniques.<br><br>Here, the data used is made accessible</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.