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1,721 results for “network data”
AHI-CALIOP Collocated Data for Training and Validation of Cloud Masking Neural Networks
<p>Collocated data between AHI at 2km resolution (nadir) and CALIOP 1km cloud product v4.20 used for training and validating cloud identification neural networks. The main training and validation data from 2019 is stored in monthly directories, whilst the collocated dataset used to compare the NN, JMA and BoM cloud mask performances is the file "superdf.h5". All collocated data is stored as .h5 files and was built using the Python Pandas package. In this archive, the data has been stored as compressed directories for each month or as a single compressed file in the case of "superdf.h5" using tar with bzip2 compression or just bzip2 compression respectively.</p>
Data sets used in "Neural network emulation of the formation of organic aerosols based on the explicit GECKO-A chemistry model"
<p>The training, validation, and testing data sets for toluene, dodecane, and alpha-pinene models described in the manuscript. A link to the manuscript will be added here when it becomes available. All trajectories in the data sets were generated using GECKO-A. The source code for using the data sets can be found at https://github.com/NCAR/gecko-ml </p>
Source data for "In-degree centrality in a social network is linked to coordinated neural activity"
<p>The following includes the source data for the manuscript titled "In-degree centrality in a social network is linked to coordinated neural activity". A second version of the source data ("Source Data_updated_011222.xlsx") includes the source data for the figures in the supplementary materials.</p>
OA eBook Usage Data Exchange Network Business Model Canvas
<p>This initial business model canvas was created to inform technical and governance roadmap discussions pertaining to the data exchange elements of the Developing a Data Trust for Open Access Ebook Usage project.</p>
TROPOMI SIF high resolution data at 0.005° for CONUS as estimated by the convolutional neural network SIFnet
<p>We develop a Convolutional Neural Network, named SIFnet, that increases the spatial resolution of SIF from the TROPOMI by a factor of 10 to a spatial resolution of 0.005°. SIFnet utilizes coarse SIF observations together with a broad range high resolution auxiliary data. The insights gained from interpretable machine learning techniques allow us to make quantitative claims about the relationships between SIF and other common parameters related to photosynthesis.</p> <p>Temporal coverage: April 2018 until March 2021, 16 day time steps</p> <p>Data for other regions can be requested and produced by the authors. Please refer for further information to: </p> <p>Gensheimer, J., Turner, A. J., Köhler, P., Frankenberg, C., & Chen, J. (2022). A Convolutional Neural Network for Spatial Downscaling of Satellite-Based Solar-Induced Chlorophyll Fluorescence (SIFnet). A convolutional neural network for spatial downscaling of satellite-based solar-induced chlorophyll fluorescence (SIFnet). <em>Biogeosciences</em>, <em>19</em>(6), 1777-1793. DOI: https://doi.org/10.5194/bg-19-1777-2022</p>
Nanosecond Optical Switching and Control System for Data Center Networks(data)
<p>This file contains the data and codes for the paper "Nanosecond Optical Switching and Control System for Data Center Networks" </p>
Supplementary data for the article "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."
<p>Supplementary figures, tables and raw data for the article " "A data-driven simulation of the trophallactic network and intranidal food flow dissemination in ants."</p> <p><strong>Food sharing can occur in both social and non-social species but is crucial in eusocial species in which only some group members collect food. This food collection but also intranidal food distribution through trophallactic (i.e. mouth-to-mouth) exchanges are fundamental issues in eusocial insects. However, the behavioural rules underlying the regulation and the dynamics of food intake and the resulting networks of exchanges are poorly understood. In this study, we provide new insights </strong><strong>into</strong><strong> the behavioural rules underlying the structure of trophallactic networks and food dissemination dynamics within the colony. We build a simple data-driven model that implements interindividual variability and division of labour to investigate the processes of food accumulation/dissemination inside the nest, both at the individual and collective </strong><strong>levels</strong><strong>. We also test the alternative hypotheses (no variability and no division of labour). Division of labour with inter-individual variability predicts contrary to other models the food dynamics and exchange networks. We establish the links between the interindividual heterogeneity of the trophallactic behaviours, the food flow dynamics and network of trophallactic events</strong>.<strong> Despite the relative simplicity of the model rules, efficient trophallactic networks may emerge as the ones observed in ants leading to better understanding of evolution of such societies.</strong></p> <p> </p>
Shared Data for De-scattering Deep Neural Network
<p>These images show mouse cortical layer 2/3 pyramidal neurons sparsely labeled with a cell fill (eYFP or mScarlet-I) to visualize the dendritic arbor, including dendritic spines. Cells were imaged in vivo using point-scan two-photon microscopy (PSTPM) and temporal focusing microscopy (TFM). These images were used for training and testing the De-Scattering Deep Neural Network (<a href="https://github.com/eleweiz/DeScattering_NN">https://github.com/eleweiz/DeScattering_NN</a>).</p>
Source data and code for manuscript 'An executive network for the control of sequence-behavior in pigeons'
<p>The contents of this folder are part of the submission of the manuscript entitled 'An executive network for the control of sequence-behavior in pigeons', by Lukas Alexander Hahn & Jonas Rose</p> <p>Contact: lukas.hahn@ruhr-uni-bochum.de</p> <p>Data and code have been compressed into a .zip folder each. Unpack the contents of the folders to use the dataset. The dataset is split into two main folders and one Matlab file:</p> <p>'code'<br> contains all analysis code to produce all figures and reported statistics of the manuscript (refer to the<br> MATLAB live script 'manuscriptResultsLiveScript.mlx' to run the analysis, please adjust the path information of where the data is stored on your computer).</p> <p>'RESULTSSTATISTICS.mat'<br> contains all reported statistical values (generated by 'manuscriptResultsLiveScript.mlx')</p> <p>'sourceData'<br> Contains all required source data files (i.e. pre-processed data) required to run the analyses stored in 'code'.</p> <p>Data related to animal behavior was recorded using MATLAB (R2016b). Electrophysiological data was recorded by NeuroNexus microelectrodes and an INTAN RHD2000 headstage on an INTAN USB-Interface board, with a sampling rate of 30 kHz and was subsequently filtered for spike sorting at bandpass 0.5 - 7.5 kHz.</p> <p>Data format is the MATLAB '.mat' type (which can be loaded in by MATLAB, or alternatively by the freely available Octave Software (https://www.gnu.org/software/octave)).<br> Data is organized in MATLAB structures, one file per session for behavioral results, one file per neuron for different alignments and preprocessing conditions (refer to manuscriptResultsLiveScript).<br> Structures contain individual matrices (labelled by a descriptive name) that contain numerical values or character strings.<br> Matrices labelled by the keyword 'Info' contain character strings that give a brief description of the loaded data.<br> Source data contains two separate folders containing data of animal 1 ('P855'), and animal 2 ('T1003').</p> <p>Data was sorted into different subsets, for analysis of individual task phases. Subfolder 'NCL' refers to 'nidopallium caudolaterale', 'NIML' refers to 'nidopallium intermedium mediale pars laterale', the recorded brain regions.</p> <p> </p>
Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads
<p>Raw Experimental Data of the Master's Thesis: Improving Serverless Edge Computing for Network Bound Workloads</p> <p>Please see the README for information on parsing and data structure.<br> Some of the data might need additional explanations. In case of confusion don't hesitate to contact me under jacob.palecek@outlook.com</p>
EM27/SUN network data CoMet 2018 Poland Silesia
<p>EM27/SUN network data sampled during the CoMet 2018 measurement campaign in the upper Silesian coal basin in Poland.</p> <p>The data set contains the raw interferograms recorded for all four sites. </p>
Network-Nanostructured ZIF-8 too Enable Percolation for Enhanced Gas Transport (Data Repository)
<p>Data repository </p>
Data from: A stochastic generative model for citation networks among academic papers
<p>We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.</p>
Operational assimilation of spectral wave data from the Sofar Spotter network
<p>Historically, the sparseness of in situ open-ocean wave and weather observations has severely limited the forecast skill of weather over the ocean with major social and economic consequences for coastal communities and maritime industries. Ocean surface waves, specifically, are important for the interaction between atmosphere and ocean, and thus key in modeling weather and climate processes. Here, we investigate the improvements achievable from a large distributed sensor network combined with advances in assimilation strategies. Wave spectra from a global network of over 600 Sofar Spotter buoys are assimilated into an operational global wave forecast via optimal interpolation to update model spectra to best fit observations. We demonstrate end-to-end improvements in forecast skill of significant wave height of 38%, and up to 45% for other bulk parameters. This shows distributed observations of the air-sea interface, with advances in assimilation strategies, can reduce uncertainty in forecasts to dramatically improve earth system modeling</p>
Data from: How structured is the entangled bank? The surprisingly simple organization of multiplex ecological networks leads to increased persistence and resilience
Species are linked to each other by a myriad of positive and negative interactions. This complex spectrum of interactions constitutes a network of links that mediates ecological communities' response to perturbations, such as exploitation and climate change. In the last decades, there have been great advances in the study of intricate ecological networks. We have, nonetheless, lacked both the data and the tools to more rigorously understand the patterning of multiple interaction types between species (i.e., "multiplex networks"), as well as their consequences for community dynamics. Using network statistical modeling applied to a comprehensive ecological network, which includes trophic and diverse non-trophic links, we provide a first glimpse at what the full "entangled bank" of species looks like. The community exhibits clear multidimensional structure, which is taxonomically coherent and broadly predictable from species traits. Moreover, dynamic simulations suggest that this non-random patterning of how diverse non-trophic interactions map onto the food web could allow for higher species persistence and higher total biomass than expected by chance and tends to promote a higher robustness to extinctions.
Data for paper: Comparing Measures of Centrality in Bipartite Social Networks: A Study of Drug Seeking for Opioid Analgesics
<p>Data and code for paper: Comparing Measures of Centrality in Bipartite Social Networks: A Study of Drug Seeking for Opioid Analgesics</p>
Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '
<p>Data of "Universal platform for scalable semiconductor-superconductor nanowire networks '</p>
Code and Data for "Bridging Gaps in the Climate Observation Network"
<p>The code and data used in the paper "Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Model"</p>
Data from: Evaluating otter reintroduction outcomes using genetic spatial capture-recapture modified for dendritic networks
<p>River otters (Lontra canadensis) were extirpated from New Mexico by the 1950s. A limited reintroduction occurred during 2008–2010 in which 33 otters sourced from Washington (WA) were translocated to the Upper Rio Grande Basin (URG) of New Mexico. We conducted a noninvasive genetic capture-recapture survey during the winter of 2018 by collecting fecal DNA samples from river otter scats found at latrines in the URG dendritic network of perennial waterways. Our objectives were to: 1) estimate genetic diversity and effective population size; 2) genetic divergence from the WA source population and potential connectivity with regionally proximal populations; 3) spatially explicit population density and size; and 4) population growth rate since the founder event. Between February and April 2018, we collected 1,184 fecal DNA samples from 622 individual scats at 20 latrines; genotyping was attempted at 10 otter-specific microsatellite loci for a subsample of 543 samples. A bottlenecking founder effect was strongly supported, which, combined with genetic drift, reduced genetic diversity and effective population size by 20–26% and 106–170%, respectively, compared with the WA source population. Estimated population density from spatial capture-recapture models was 0.23–0.28 otter/km of waterway, or 1 otter/3.57–4.35 km of waterway, corresponding to a total population size of 83–100 otters across 359 km of the perennial dendritic network from La Mesilla, New Mexico to Alamosa National Wildlife Refuge, Colorado. Estimated average annual population growth rate since the founder event was 1.12–1.15/year. Despite successful population establishment, the URG river otter population remains small, is genetically degraded, and does not yet meet the criteria for long-term reintroduction success. Projections suggested that the population could reach the recommended minimum viable population size of ≥400 otters by the years 2030–2033, though sufficient habitat may not exist in the URG Basin to support that many otters. </p>
Supplementary Data - Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer network
<p><strong>Supplementary data 1</strong>: Peak annotation evaluation between MetDNA1 and KGMN (MetDNA2)</p> <p><strong>Supplementary data 2</strong>: 46 standard mixture (46std_mix) and the knowledge-based metabolic reaction network</p> <p><strong>Supplementary data 3</strong>: KGMN results of 46std_mix data set and validation results</p> <p><strong>Supplementary data 4</strong>: KGMN results of NIST urine data sets and validation results</p> <p><strong>Supplementary data 5</strong>: KGMN results of different biological samples</p> <p><strong>Supplementary data 6</strong>: Recurrent unknowns of NIST urine via repository-mining</p> <p><strong>Supplementary data 7</strong>: Table of adducts, neutral losses, empirical rules in KGMN</p>
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.