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9,152 results for “Networking”
Raw datasets for paper "Multi-scale hydraulic graph neural networks for flood modelling"
<p>The repository contains two zip folders for the synthetic and case study datasets (raw_datasets_mesh.zip, raw_datasets_dk15.zip). </p> <p>Each zip folder comprises 4 subfolders (DEM, Geometry, Hydrograph, Simulations), containing the elevation, boundary polygon, discharge hydrograph, and full hydrodynamic results for all simulations.</p> <p>The overview.csv file provides the seeds used for experiment replicability and the runtime of the numerical model on each simulation.</p>
Dataset of "3D generative adversarial networks for turbulent flow estimation from wall measurements"
<p>Dataset of the article 'Three-dimensional generative adversarial networks for turbulent flow estimation from wall measurements' (https://doi.org/10.1017/jfm.2024.432). The codes processing data here are on https://github.com/erc-nextflow/3D-GAN.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 949085, NEXTFLOW). Views and opinions expressed are, however, those of the authors only, and do not necessarily reflect those of the European Union or the ERC. Neither the European Union nor the granting authority can be held responsible for them. A.C.M. acknowledges financial support from the Spanish Ministry of Universities under the Formación de Profesorado Universitario (FPU) programme 2020. R.V. acknowledges financial support from ERC (grant agreement no. 2021-CoG-101043998, DEEPCONTROL).</p>
Supporting data ocean model GMD submission: From Weather Data to River Runoff: Leveraging Spatiotemporal Convolutional Networks for Comprehensive Discharge Forecasting
<p>Ocean model salinity data used for the comparison of the ConvLSTM river runoff model and the original E-HYPE based model simulations.</p>
Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks
Open the record for dataset details and reuse information.
Experimental online quantum dots charge autotuning using neural networks - Output data
<p>Outputs of the model training and the online autotuning experiments presented in the paper: "Experimental online quantum dots charge autotuning using neural networks".</p> <p>Each folder in the zipped files represent a run that includes:</p> <ul> <li>log file</li> <li>plots / images</li> <li>run settings</li> <li>performance results</li> <li>pytorch model parameters</li> </ul> <p>See README.txt for more information about the file strucutre.</p>
Network files and Python code used in "Designing a sector-coupled European energy system robust to 60 years of historical weather data"
<p><strong>Description</strong></p> <p>This repository contains data presented in the paper <a href="https://www.nature.com/articles/s41467-024-54853-3" target="_blank" rel="noopener">Designing a sector-coupled European energy system robust to 60 years of historical weather data</a>. It contains the derived metrics (.csv) files from a:</p> <ol> <li>joint capacity and dispatch optimization with weather years (design years) from 1960 to 2021 as input</li> <li>dispatch optimization of the 62 capacity layouts using weather years (operational years) different from the design year.</li> </ol> <p>All results from (1) are found in "Capacity_optimization.zip" and results from (2) are found in "Dispatch_optimization.zip".</p> <p>The resulting network files (both from the capacity and dispatch optimization) are located <a href="https://anon.erda.au.dk/cgi-sid/ls.py?share_id=DuGvDWlkeI">here</a>.</p> <p>We also provide the Python code used to derive the metrics and to create the visualizations included in the paper. This is located in "Jupyter_notebooks". The Jupyter notebooks refer to Python scripts located <a href="https://github.com/ebbekyhl/multi-weather-year-assessment">here</a>.</p> <p><strong>Revisions:</strong></p> <p>This version includes the following additions compared to the previous versions: </p> <ul> <li>Timeseries of nodal loads for all years</li> <li>Timeseries of nodal heat pump Coefficient of Performance (COP) </li> <li>Nodal capacity and hourly capacity factors </li> </ul>
Datasets from "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks". JGR Biogeosciences.
<p>This repository contains the datasets linked to "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks" (Journal of Geophysical Research: Biogeosciences, <a href="https://doi.org/10.1029/2024JG008047">https://doi.org/10.1029/2024JG008047</a>). This includes:</p> <ul> <li>The images and annotation* text files (in YOLO format) used for the detection of siliceous microfossils. </li> <li>The models and training results for the trainings presented in the main text and supporting information.</li> </ul> <p>*Note that while annotations were attributed to 14 general categories, only twelve of these were used during training (<em>i.e. </em>Pennate, Centric, Silicoflagellate, Centric_debris, Spore, Other_Biomin, Cocco, Silicoflagellate_debris, Undetermined_silica, Chateoceros_Bacteriastrum, Calcispheres, Foraminifer), and all were pooled into a single "Microfossil" category for the purpose of the training.</p>
Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene co-expression networks
<p>Data and Inferred Networks accompanying the manuscript entitled - “Aggregation of recount3 RNA-seq data improves the inference of consensus and context-specific gene co-expression networks” </p> <p>Authors: Prashanthi Ravichandran, Princy Parsana, Rebecca Keener, Kaspar Hansen, Alexis Battle </p> <p>Affiliations: Johns Hopkins University School of Medicine, Johns Hopkins University Department of Computer Science, Johns Hopkins University Bloomberg School of Public Health</p> <p>Description: </p> <p>This folder includes data produced in the analysis contained in the manuscript and inferred consensus and context-specific networks from graphical lasso and WGCNA with varying numbers of edges. Contents include:</p> <ul> <li> <p>all_metadata.rds: File including meta-data columns of study accession ID, sample ID, assigned tissue category, cancer status and disease status obtained through manual curation for the 95,484 RNA-seq samples used in the study. </p> </li> <li> <p>all_counts.rds: log2 transformed RPKM normalized read counts for 5999 genes and 95,484 RNA-seq samples which was utilized for dimensionality reduction and data exploration </p> </li> <li> <p>precision_matrices.zip: Zipped folder including networks inferred by graphical lasso for different experiments presented in the paper using weighted covariance aggregation following PC correction.</p> </li> <ul> <li> <p>The networks can be found as follows. First, select the folder corresponding to the network of interest - for example, Blood, this will then include two or more folders which indicate the data aggregation utilized, select the folder corresponding appropriate level of data aggregation - either all samples/ GTEx for blood-specific networks, this includes precision matrices inferred across a range of penalization parameters. To view the precision matrix inferred for a particular value of the penalization parameter X, select the file labeled lambda_X.rds</p> </li> <li> <p>For select networks, we have included the computed centrality measures which can be accessed at centrality_X.rds for a particular value of the penalization parameter X. </p> </li> <li> <p>We have also included .rds files that list the hub genes from the consensus networks inferred from non-cancerous samples at “normal_hubs.rds”, and the consensus networks inferred from cancerous samples at “cancer_hubs.rds”</p> </li> <li> <p>The file “context_specific_selected_networks.csv” includes the networks that were selected for downstream biological interpretation based on the scale-free criterion which is also summarized in the Supplementary Tables. </p> </li> </ul> <li> <p>WGCNA.zip: A zipped folder containing gene modules inferred from WGCNA for sequentially aggregated GTEx, SRA, and blood studies. Select the data aggregated, and the number of studies based on folder names. For example, blood networks inferred from 20 studies can be accessed at blood/consensus/net_20. The individual networks correspond to distinct cut heights, and include information on the cut height used, the genes that the network was inferred over merged module labels, and merged module colors. </p> </li> </ul>
DISCERN 2: Duke Innovation & SCientific Enterprises Research Network
<p>The DISCERN dataset was developed to support academic research on corporate innovation by linking data on U.S. publicly listed firms from Standard & Poor’s Compustat database to their patents and scientific publications. A key feature of DISCERN is its comprehensive coverage of firms’ subsidiaries and their ownership changes over time, which is crucial for accurately mapping corporate innovation. Patents and publications may be assigned to various legal entities within a firm’s organizational structure. Subsidiaries may change ownership in M&A events. By accounting for these ownership linkages over time, DISCERN enables researchers to construct more precise measures of firms’ knowledge production and examine the factors influencing their R&D investment decisions.</p> <p>Version 2.0 incorporates several key improvements over the previous version of DISCERN. First, we shift to using the PatentsView database as the main source of patent data and OpenAlex as the main source of scientific publication data. PatentsView is publicly available and continuously maintained directly by the United States Patents & Trademarks Office (USPTO). OpenAlex is currently the only open data source of scientific publication metadata. Using freely available data sources allows us to share both the patent and the publication datasets openly. This enhances data access, which was previously limited due to the use of propriety data. Second, the updated dataset now covers the period from 1980 to 2021, providing an additional six years of data. Third, we transition to using Securities and Exchange Commission (SEC) filings as the primary source of subsidiary data, allowing us to trace ownership linkages further back to the mid-1990s and ensuring a higher degree of reliability compared to the Orbis data used in the original version, which was less reliable and had comprehensive coverage only from 2008. Finally, by transitioning to PatentsView and additional data sourced from the USPTO, we expand the scope of the dataset to include pre-grant patent applications and patent re-assignment information. This addition allows users to study patent applications regardless of grant status and to observe ownership transitions beyond those related to mergers and acquisitions.</p> <p>A special thanks and appreciation go to Sanskriti Purohit and Ron Rabi for their diligent work and dedication to this effort.</p> <p>The dataset is freely available under the<a href="https://cdla.dev/open-use-of-data-agreement-v1-0/"> O-UDA-1.0 License</a>, permitting unrestricted use for research and commercial purposes. We request that users provide proper citations when utilizing the dataset. The license also allows for the creation of derivative datasets based on DISCERN, with the condition that creators ask their downstream users to cite the original authors appropriately.</p> <p><strong>If you use the data, please add these citations:</strong></p> <p>1. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, Shin, H.M. & Shvadron, D. (2024). DISCERN 2.0: Duke Innovation & SCientific Enterprises Research Network [Dataset]. In Zenodo (CERN European Organization for Nuclear Research). <a href="../doi/10.5281/zenodo.3594642">https://doi.org/10.5281/zenodo.3594642</a></p> <p>2. Arora, A., Belenzon, S., Cioaca, L., Sheer, L, & Shvadron, D. (2024). Back to the Future: Are Big Firms Regaining their Scientific and Technological Dominance? Evidence from DISCERN 2.0 (available soon)</p> <p> </p>
Support data for article "The concept of optimal planning of a linearly oriented segment of the 5G network"
<p>Support data for article</p> <p>V. Kovtun, K. Grochla, E. Zaitseva, and V. Levashenko, “The concept of optimal planning of a linearly oriented segment of the 5G network,” PLOS ONE, vol. 19, no. 4. Public Library of Science (PLoS), p. e0299000, Apr. 17, 2024. doi: 10.1371/journal.pone.0299000.</p> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
Support data for article "The concept of network resource control of a 5G cluster focused on the smart city's critical infrastructure needs"
<p>Support data for article:</p> <div>V. Kovtun, K. Grochla, and K. Połys, “The concept of network resource control of a 5G cluster focused on the smart city’s critical infrastructure needs,” Alexandria Engineering Journal, vol. 94. Elsevier BV, pp. 248–256, May 2024. doi: 10.1016/j.aej.2024.03.038.</div> <p>This research is part of the project No. 2022/45/P/ST7/03450 co-funded by the National Science Centre and the European Union Framework Programme for Research and Innovation Horizon 2020 under the Marie Skłodowska-Curie grant agreement No. 945339.</p>
TNF SIGNALING NETWORK FUNCTIONS
<p>Tumor Necrosis Factor (TNF) inhibitors are a class of medications that target and neutralize TNF, a pro-inflammatory cytokine that plays a key role in the immune response and inflammation. TNF is involved in the pathogenesis of various autoimmune and inflammatory diseases, such as rheumatoid arthritis, psoriasis, ankylosing spondylitis, and inflammatory bowel disease. By inhibiting TNF, these drugs can effectively reduce inflammation, decrease joint damage, and improve clinical symptoms in patients with these conditions. TNF inhibitors work by blocking the interaction of TNF with its receptors, thereby preventing the cascade of inflammatory processes that lead to tissue damage. Understanding the mechanisms and therapeutic effects of TNF inhibitors is essential for optimizing the management of chronic inflammatory diseases.</p>
Neural network prediction of strong lensing systems with domain adaptation and uncertainty quantification
<p>This project combines the emerging field of Domain Adaptation with Uncertainty Quantification, working towards applying machine learning to real scientific datasets with limited labelled data. For this project, simulated images of strong gravitational lenses are used as source and target dataset, and the Einstein radius θ E and its uncertainty are determined through regression.</p> <p>Applying machine learning in science domains such as astronomy is difficult. With models trained on simulated data being applied to real data, models frequently underperform - simulations cannot perfectlty capture the true complexity of real data. Enter domain adaptation (DA). The DA techniques used in this work use Maximum Mean Discrepancy (MMD) Loss to train a network to being embeddings of labelled "source" data gravitational lenses in line with unlabeled "target" gravitational lenses. With source and target datasets made similar, training on source datasets can be used with greater fidelity on target datasets.</p> <p>Scientific analysis requires an estimate of uncertainty on measurements. We adopt an approach known as mean-variance estimation, which seeks to estimate the variance and control regression by minimizing the beta negative log-likelihood loss. To our knowledge, this is the first time that domain adaptation and uncertainty quantification are being combined, especially for regression on an astrophysical dataset.</p>
Trait Networks: Assessing Marine Community Resilience and Extinction Recovery
<p>Data and R code for Trait networks. From the paper:</p> <p> </p> <p><strong>Trait Networks: Assessing Marine Community Resilience and Extinction Recovery</strong></p> <p><strong>Summary</strong></p> <p><strong>Extensive global habitat degradation and the climate crisis are tipping the biosphere towards a “sixth” mass extinction and marine communities will not be spared from this catastrophic loss of biodiversity. The resilience of marine communities following large-scale disturbances or extinction events is mediated by the life-history traits of species and their interplay within communities. The presence and abundance of traits in communities provide proxies of function, but whether the breakdown of their associations with species loss can delineate functional loss remains unclear. Here, we propose that relationships between traits within trait networks provide unique perspectives on the importance of specific traits, trait combinations, and their role in supporting the stability of communities, whilst allowing quantification of the vulnerability of both past deep time and present-day marine communities.</strong></p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 3
<p>LENS dataset 2024-06 Part 3</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 2
<p>LENS dataset 2024-06 Part 2</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 3
<p>LENS dataset 2024-07 Part 3</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 2
<p>LENS dataset 2024-07 Part 2</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 1
<p>LENS dataset 2024-07 Part 1</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 1
<p>LENS dataset 2024-06 Part 1</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</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.