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9,152 results for “Networking”
Spatiotemporal Modeling of Mitochondrial Network Architecture
<p>Mitochondrial fusion events and their classification into three categories (tip-tip, tip-side, side-side) acquired on widefield, confocal, instant structured illumination and structured illumination microscopes.</p> <p>This data was used as comparison to modelled, theoretical fusion rates and their contributions to the maintenance of mitochondrial network morphologies.</p>
Inductive biases of neural network modularity in spatial navigation
<p>The brain may have evolved a modular architecture for reward-based learning in daily tasks, with circuits featuring functionally specialized modules that match the task structure. We propose that this architecture enables better learning and generalization than architectures with less specialized modules. To test this hypothesis, we trained reinforcement learning agents with various neural architectures on a naturalistic navigation task. We found that the architecture that largely segregates computations of state representation, value, and action into specialized modules enables more efficient learning and better generalization. The behavior of agents with this modular architecture also resembles macaque behaviors more closely. Investigating the latent state computations in these agents, we discovered that the learned state representation combines prediction and observation, weighted by their relative uncertainty, akin to a Kalman filter. These results shed light on the possible rationale for the brain's modular specializations and suggest that artificial systems can use this insight from neuroscience to improve learning and generalization in natural tasks.</p>
Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets
<p>The uploaded files are source datasets for the scMTNI algorithm. scMTNI is a multi-task learning framework that integrates the cell lineage structure, scRNA-seq and scATAC-seq measurements to enable joint inference of cell type-specific GRNs. See more details at Zhang, S., Pyne, S., Pietrzak, S. et al. Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets. Nat Commun 14, 3064 (2023). <a href="https://doi.org/10.1038/s41467-023-38637-9">https://doi.org/10.1038/s41467-023-38637-9</a></p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/scMTNI_sourcedata.tar.gz?versionId=a19f69c1-5bb2-436e-9a30-a7efbf1ab3da">scMTNI_sourcedata.tar.gz</a> contains the following 3 parts:</p> <p>1) The cluster-specific scRNA-seq matrices and the prior networks for all three datasets and scMTNI inferred consensus networks.</p> <p>2) Gold standard human and mouse datasets for evaluation.</p> <p>3) Source data for scMTNI figures 2-8 and supplementary figures. The key for each figure and its corresponding file path is in SourceData_Key_v2.xlsx.</p> <p>The source data <a href="../api/files/13d4a93c-bf23-47c9-ae64-92bcfd9f8772/Buenrostro_Hematopoiesis.tar.gz">Buenrostro_Hematopoiesis.tar.gz</a> contains the scRNA-seq data for human hematopoietic differentiation downloaded from Data S2 of Buenrostro et al.</p> <p>The source data <a href="../api/files/5c75876b-c08a-4186-a08d-da9db78f64f3/RawMotifFiles.tar.gz">RawMotifFiles.tar.gz</a> contains the motif instance files and promoter files for human and mouse for generating prior networks using scATAC-seq data for scMTNI. Check <a href="https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md">https://github.com/Roy-lab/scMTNI/blob/master/Scripts/genPriorNetwork/readme.md</a> for examples and scripts.</p> <p>The <a href="../api/records/11876980/draft/files/Buenrostro_priorNetwork_bamfiles.tar.gz/content">Buenrostro_priorNetwork_bamfiles.tar.gz</a> contains the raw bam files of scATAC-seq data for human hematopoietic differentiation downloaded from Buenrostro et al.</p>
Lausanne environmental data : Sentinel L1C Lausanne region data - NDVI, ARVI indices; Walkable network
<p>Multi-spectral band images captured by the Sentinel L1C satellites in August 2016 over Lausanne, Switzerland. Bands B02, B03, B04, B08 projected according to the Swiss coordinates system (EPSG:21781- CH1903 / LV03 -- Swiss CH1903 / LV03) and associated calculated ARVI and NDVI vegetation indices.</p> <p>Walkable network (2021) from OpenStreeMap using OSMnx python package</p>
HerpesFolds: All-versus-all protein interaction network
<p>For HSV-1, HCMV, and KSHV, all possible protein-protein combinations within the species were structurally predicted with AlphaFold 2.3. A user interface and network visualization can be found at: https://www.bosse-lab.org/herpesfolds/</p>
Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks - Supporting Dataset
<div>Supporting dataset for the numerical experiments in the manuscript <em>Mesh Motion In Fluid-Structure Interaction With Deep Operator Networks</em>, consisting of a tar.gz archive containing the following directories:</div> <h3>learnext_dataset</h3> <div>Dataset used to train the DeepONet mesh motion model. For one period of structure deformation in the FSI benchmark problem 2 of Turek and Hron (2006), contains the harmonic mesh motion in input and biharmonic mesh motion in output, relative to the undeformed domain.</div> <h3>mesh</h3> <div>Mesh of the FSI benchmark problem 2 used to run FSI simulations to test DeepONet mesh motion.</div> <h3>Warmstart checkpoint</h3> <div>State checkpoint of FSI benchmark problem 2 run for 15 simulation seconds with trained DeepONet mesh motion. Used to warmstart the FSI simulations to verify quantities of interest produced from DeepONet mesh motion by comparing it with ones from biharmonic mesh motion.</div> <h3>grav-test</h3> <div>Dataset used in gravity-driven deformation test of DeepONet mesh motion.</div> <h3>best_run_model</h3> <div>Saved, pretrained branch and trunk networks from the best run of the hyperparameter study and problem-file needed to build the DeepONet mesh motion from it.</div>
Evaluating Cloud Properties Across New Zealand: Ceilometer Network Data
<p><span>A network of 16 Vaisala CL31 ceilometers operated by MetService at airports across New Zealand had data available and were processed using ALCF with a cloud threshold mask of 6e-6. Nine sites were in Te Ika-a-Māui / North Island, six in Te Waipounamu / South Island and one site on Chatham Island. Metadata for the 16 sites are available in the Processed Ceilometer Medata.csv file.<br></span></p>
Dataset to accompany publication "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"
<h2>Dataset description</h2> <p>This dataset displays the raw data for the manuscript "Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy"<strong> </strong>published in <em>Precision Chemistry </em>on 06 December 2023 (DOI: <a title="DOI URL" href="https://doi.org/10.1021/prechem.3c00078">10.1021/prechem.3c00078</a>).</p> <p> </p>
From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil
<p>Dataset belonging to publication 'From nucleation to fat crystal network: effect of stearic-palmitic sucrose ester on static crystallization of palm oil', <a href="https://doi.org/10.3390/foods13091372">https://doi.org/10.3390/foods13091372</a>.</p> <p> </p> <p>PLM = polarized light microscopy</p> <p>CryoSEM = cryo-scanning electron microscopy</p> <p>> data obtained after de-oiling fat samples with isobutanol (4x) and aceton (1x), see publication</p> <p>SAXS = small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>WAXS = wide-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of empty capillary, see publication</p> <p>> for SE heating and cooling cycles, data is recorded from 70°C (1h) to 20°C (1h), and 4 repeated cycles </p> <p>USAXS = ultra-small-angle X-ray scattering</p> <p>> data obtained after subtraction of intensity of the capillary at 70°C, see publication</p> <p>DSC = differential scanning calorimetry</p> <p>> Samples are heated at 70°C for 10 min, and then crystallized following a certain protocol (see publication).</p> <p>> Samples are maintained one hour at their respective isothermal crystallization temperature.</p> <p>> Samples are rehaeted at 5°C/min to 70°C.</p> <p>SE = sucrose ester (SP30, HLB6)</p> <p>PO = palm oil</p> <p>POE = palm oil + 0.5 wt% SE</p> <p>FC = fast cooling (20°C/min)</p> <p>SC = slow cooling (1°C/min)</p>
Data for: Facile Preparation of Tunable Polyborosiloxane Networks via Hydrosilylation
<p>Polyborosiloxanes are used in a variety of fields due to their unique and useful dynamic properties. Traditionally, crosslinked polyborosiloxanes are prepared by incorporating boric acid into siloxane pre-polymers, a process that is time consuming, energy intensive, and challenging due to the immiscibility of the reagents. Here, we report a versatile synthetic method to rapidly cure polyborosiloxane networks via hydrosilylation of chain-end or backbone functionalized polydimethylsiloxane (PDMS) derivatives with an inexpensive trivinylboronate. Networks synthesized from these readily available building blocks cure in ~2 minutes at convenient temperatures (<em>e.g.</em>, 90 °C) and exhibit enhanced viscoelastic behavior when compared to traditional polyborosiloxane networks fabricated via the conventional condensation route. By virtue of using efficient hydrosilylation chemistry, another key advantage of this synthetic platform is the ability to synthesize dynamic polyborosiloxanes with different network connectivity by simply using silicones with Si–H moieties placed at the chain ends (telechelic) or distributed throughout the repeat-unit structure (copolymers). The availability of other alkenes amenable to hydrosilylation provides an additional formulation handle to synthesize mixed dynamic–static networks with tunable control over stress relaxation and solvent resistance. In summary, the synthetic approach disclosed herein is a simple and accessible platform for preparing dynamic polyborosiloxanes with tunable material properties.</p>
Zola Character Networks Dataset
<p>This dataset contains:</p> <ul> <li>The index of character occurrences in the 20 novels (La Pléiade edition)</li> <li>20 character networks based on the novels composing Émile Zola's Les Rougon-Macquart, written between 1870 and 1893</li> </ul> <p>This dataset was used for a <a href="https://serval.unil.ch/fr/notice/serval:BIB_E4427AF55A80" rel="nofollow">conference paper at the Digital Humanities 2015 conference in Sydney</a>.</p> <p>The networks were built with <a href="https://github.com/yrochat/phd_thesis">the method of co-occurrences developed in my PhD thesis</a> (2014). I used R and <a href="https://github.com/igraph/rigraph">igraph</a>.</p> <p>The (undirected) character networks are available as csv files, with three columns: the third column is for the weights (see the <a href="https://serval.unil.ch/fr/notice/serval:BIB_663137B68131" rel="nofollow">PhD thesis</a> for more explanations about the computation).</p> <p>The index of characters is available at the end of La Pléiade edition of Les Rougon-Macquart. It was digitised and corrected so that names are recognisable. Some errors remain, but it doesn't have an impact on the data.</p>
SN2N's 3D dataset of outer mitochondrial membrane network of live COS-7 cells labeled with Tom20-mCherry on SD-SIM sysytem.
Open the record for dataset details and reuse information.
The output data of the 1D Venusian chemistry-diffusion model of Dai et al. (2024) and the adopted chemical network
<p>To use these data, please cite the paper: Dai et al. (2024, doi: 10.1051/0004-6361/202450552) </p> <p>Supplemental_Tables_for_Dai_et_al_2024.pdf: the chemical network adopted in the model</p> <p>Nominal.txt: the chemical network adopted in the model</p> <p>modify_chem.py: the additional adjustments of the reactions</p> <p>Nominal_Bkzz_SO2.vul and A_Dkzz_SO2.vul: the output data of the nominal model and model A, respectively</p> <p>reading_data.py: the methods to read the output files</p> <p> </p> <p>*Errata: </p> <p>1) R296 in Supplemental Table: "1×10^7+0.05n_atm" should have been "1×10^17+0.05n_atm"</p> <p>2) R329 in Supplemental Table: should have been removed</p> <p>The errata do not affect the results of this study.</p>
Data set in the form a relational database (sql) to denote a network of service providers, service clients and recommenders
<p>This data-set pertains to a network (i.e. graph) represented in the form of a relational data-base of service providers (nodes), service clients (nodes), service recommenders (nodes) and relationaships between then (i.e. a client used a provider, a recommender recommended a service to another client), along with some initial values of the QoS level perceived by any client whi have used a service and the reputation of a recommender. The data-set can be used for developing a reputation-based trust system. </p>
Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations.
<p>Repository containing dataset and code for the manuscript entitled <em>Downscaling mutualistic networks from species to individuals reveals consistent interaction niches and roles within plant populations</em>.</p> <p>For this study, we compiled 46 empirical individual-based networks on plant-animal seed dispersal mutualism, encompassing 1037 plant individuals across 29 species from various regions. We compare the structure of individual-based networks to that of species-based networks and by extending the niche concept to interaction assemblages, we explore levels of individual plant specialization. We examine how individual variation influences network structure and how plant individuals "explore" the interaction niche of the population.</p> <p>Please refer to <strong>makefile.R</strong> for project outline, explanation and codes used, and to the <strong>README</strong> in networks folder for data structure and compilation.</p>
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
CD4+ T cells re-wire granuloma cellularity and regulatory networks, promoting immunomodulation following Mtb reinfection
<p>Here we include the necessary .ipynb, .h5ad, and .rds (used for cell-cell interaction analyses) files used in our work: "Immunomodulatory re-wiring of granuloma cellularity and regulatory networks by CD4 T cells following Mtb reinfection"</p>
Wearable Network for Multi-Level Physical Fatigue Prediction in Manufacturing Workers - Dataset
<p>This dataset contains data from 43 subjects following two authentic manufacturing protocols and their self-reported fatigue score. The system employs 6 wearable sensors to continuously track vital and locomotive signs from multiple body locations.</p> <p> </p> <p>The goal of the experiment is to predict fatigue trends in a subject, while they are asked to perform pre-defined manual tasks simulating a manufacturing environment, using data from soft, flexible, wearable sensors and a vision system. The tasks in this study are repetitive and physically exerting involving intricate steps taken in real manufacturing settings. The iterative nature of the tasks facilitates comparative analyses of distinct temporal segments to characterize fatigue. The two manufacturing tasks are (1) Task Composite: Composite Sheet Layup, and (2) Task Harnessing: Wire Harnessing. The task protocol requires the subject to wear sensors to monitor vital and locomotive signs continuously. Additionally, we incorporate a weighted vest to exaggerate the induced fatigue in a reasonable duration for the study to mimic a full shift for a manufacturing worker. Each task consists of two rest periods of 5 minutes each at the start and end, as well as five segments of physical tasks. On average, each task takes a total of 1 hour. Before each data segment, the subject fills out a survey form to indicate their current fatigues as per the Borg scale. </p>
Thermodynamics of alkali feldspar solid solutions with varying Al–Si order: atomistic simulations using a neural network potential - Accompanying Data
<p>This dataset accompanies the manuscript: "Thermodynamics of alkali feldspar solid solutions with varying Al–Si order: atomistic simulations using a neural network potential". It contains:</p> <ul> <li>LAMMPS-data files of the relaxed 8x6x8 systems for the three ordering types across Na-K composition, </li> <li>template input files for the minimization and for the semi grand canonical Monte Carlo + molecular dynamics simulation,</li> <li>the training and testing data with and without the point charge correction,</li> <li>the neural network potential committee and a modified n2p2 source that is necessary for running the special weighted atom centered symmetry functions. </li> </ul> <p>The algorithm to create the Al-Si and Na-K disorder is hosted on <a href="https://github.com/alexgorfer/Alkali-feldspar-disorder-generator">https://github.com/alexgorfer/Alkali-feldspar-disorder-generator</a> instead.</p>
Write-only File System for Privacy-aware Wireless Sensor Networks Evaluation Dataset
<p>Evaluation dataset for the paper <strong>"WoFS: A Write-only File System for Privacy-aware Wireless Sensor Networks"</strong> published at the <em>49th IEEE Conference on Local Computer Networks (2024)</em></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.