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9,152 results for “Networking”
Network Tracking Dataset
<p>A dataset for Multi-Sensor Network Tracking Research</p>
Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"
<p>Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks".</p> <p>The code to use this dataset is publicly available at <a href="https://github.com/hshose/soeampc">https://github.com/hshose/soeampc</a></p> <p>The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system.</p> <p>For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving.</p> <p>The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.</p>
Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Ensemble Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European marine species based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.5° Resolution. The data report, for each 0.5° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell.</p>
Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast
<p>Supplemental Material for Manuscript: The Heterogeneity of Social Network and Institutional Covariance in the American Southeast</p>
Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)
<p>Data for "Randomness in Local Optima Network Sampling" (GECCO Companion Proceedings, 2023)</p>
Figure 2. Haplotype median joining network estimated from dataset 1, comprising 76 in Phylogeography and evolutionary lineage diversity in the small-eared greater galago, Otolemur garnettii (Primates: Galagidae)
Figure 2. Haplotype median joining network estimated from dataset 1, comprising 76 samples of partial cytochrome b (402 bp).
CESNET-USTS23: a benchmark dataset of Unevenly spaced time series from network traffic
<p>This dataset was created to evaluate characteristics of <em>Unevenly sampled time series from network traffic (USTS)</em> for the paper <em>Unevenly Spaced Time Series from Network Traffic</em>.</p> <p>The file named <code>time_series.tar.gz</code> contains a folder with time series CSV files as raw data of the experiment. In the folder are the following files:</p> <ul> <li><code>fts.csv</code> -- contains 2.6 million <em>Flow time series (FTS)</em> created from 259 million IP flows,</li> <li><code>pts.csv</code> -- contains 19 million <em>Packet time series (PTS)</em> created from 110 million network packets,</li> <li><code>sfts.csv</code> -- contains 15 million <em>Single flow time series (SFTS)</em> created from 160 million network packets.</li> </ul> <p>Traffic was captured on the national CESNET2 network from February 2023 to April 2023. All IP addresses in the dataset were anonymized.</p> <p>The <code>fts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Flow time series. (real IP address was anonimized by replacing with a random IP address)</li> <li>N_FLOWS -- Number of flows in time series, i.e., number of data points.</li> <li>N_PACKETS -- Number of packets in time series, i.e., the sum of metric PACKETS.</li> <li>N_BYTES -- Number of bytes in time series, i.e., the sum of metric PACKETS.</li> <li>PACKETS -- The array containing the time series metric number of packets in the IP flow.</li> <li>BYTES -- The array containing the time series metric number of bytes in the IP flow.</li> <li>START_TIMES -- The array containing the time series time axis of the flows starts.</li> <li>END_TIMES -- The array containing the time series time axis of the flows ends.</li> </ul> <p>The <code>pts.csv</code> has the following format:</p> <ul> <li>ID_DEPENDENCY -- Identification of a network dependency observed as a Packet time series. (real IP address was anonymized by replacing with a random IP address)</li> <li>BYTES -- The array containing the time series metric payload length of the network packet.</li> <li>TIMES -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The <code>sfts.csv</code> has the following format:</p> <ul> <li>SRC_IP -- Source IP address. (real IP address was anonimized by replacing with a random IP address)</li> <li>SRC_PORT -- Source port.</li> <li>DST_IP -- Destination IP address (real IP address was anonymized by replacing with a random IP address)</li> <li>DST_PORT -- Destination port.</li> <li>bytes -- The array containing the time series metric payload length of the network packet.</li> <li>time -- The array containing the time series time axis of the transmission of network packets.</li> </ul> <p>The file named <code>characteristics.tar.gz</code> contains a folder with characteristics gained by experiments from time series files. In the folder are the following files:</p> <ul> <li><code>fts.characteristics.csv</code> -- Characteristics about Flow time series from the fts.csv.</li> <li><code>pts.characteristics.csv</code> -- Characteristics about Packet time series from the pts.csv.</li> <li><code>sfts.characteristics.csv</code> -- Characteristics about Single flow time series from the sfts.csv.</li> </ul> <p>The <code>fts.characteristics.csv</code> has the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H_BYTES -- Hurst exponent of the source time series metric BYTES.</li> <li>STATIONARITY_PACKETS -- Stationarity of the source time series metric PACKETS.</li> <li>STATIONARITY_BYTES -- Stationarity of the source time series metric BYTES.</li> <li>OVERALL_STATIONARITY -- Overal stationarity created by merging STATIONARITY_PACKETS and STATIONARITY_BYTES.</li> </ul> <p>The <code>pts.characteristics.csv</code> and <code>sfts.characteristics.csv</code> have the following format:</p> <ul> <li>LENGTH -- Number of data points in the source time series.</li> <li>DURATION -- Duration of the source time series.</li> <li>H -- Hurst exponent of the source time series.</li> <li>STATIONARITY -- Stationarity of the source time series.</li> </ul> <p>We provide the samples of all zipped files for a quick lookup: <code>fts.characteristics.sample.csv</code>, <code>fts.sample.csv</code>, <code>pts.characteristics.sample.csv</code>, <code>pts.sample.csv</code>, <code>sfts.characteristics.sample.csv</code>, <code>sfts.sample.csv</code></p> <p> </p>
Seismic structure beneath the Avacha and Koryaksky volcanoes in Kamchatka based on the data of permanent and temporary networks
<p>This file contains the files to reproduce the results presented in the article: <strong>Seismic structure beneath the Avacha and Koryaksky volcanoes in Kamchatka based on the data of permanent and temporary networks </strong>by Kitsura E., Koulakov I., Jakovlev A., Abkadyrov I., Bushenkova N., Chebrov D., Izbekov P., and Qaysi S.I., submitted to <em>Journal of Geophysical Research, Solid Earth</em>.</p> <p>This file includes:</p> <p>1. The full folder with the LOTOS code for the passive-source seismic tomography (Koulakov, 2009, BSSA). </p> <p>2. Folder with the dataset including arrival times of the P and S waves from local seismicity in the area of the Avacha group of volcanoes.</p> <p>3. README_AVA__KOR.pdf file with the description of the workflow on how to reproduce the tomography models based on experimental and synthetic data presented in the article. </p> <p>Koulakov, I., 2009, LOTOS code for local earthquake tomographic inversion: Benchmarks for testing tomographic algorithms: Bulletin of the Seismological Society of America, v. 99, p. 194–214, https://doi.org/10.1785/0120080013.</p>
Chemical reaction networks of glycolonitrile and glycolonitrile-H
<p>Reaction networks of glycolonitrile (HOCH<sub>2</sub>CN) and HOCH<sub>2</sub>CHN radical obtained with the automated reaction discovery program <a href="https://rxnkin.usc.es/index.php/AutoMeKin">AutoMeKin</a></p>
Maximally Error-Tolerant MZI Mesh-based Optical Neural Networks
<p>Codes.zip contains code that trains and tests maximally error-tolerant Mach-Zehnder mesh optical neural networks on MNIST, FashionMNIST, and KMNIST.</p> <p>models.zip contains all the trained model parameters.</p>
Supplemental Material for Manuscript: A Network Approach to Zooarchaeological Datasets and Human-Centered Ecosystems
<p>Supplemental Material for Manuscript: A Network Approach to Zooarchaeological Datasets and Human-Centered Ecosystems</p>
Frugivory and seed dispersal in the Cerrado: Network structure and defaunation effects
<p><span>Seed dispersal is a fundamental process that is highly threatened by the rapid decline of large-bodied frugivores worldwide. The Brazilian Cerrado, the largest savanna in the world, represents an ideal site for investigating seed dispersal because of its biodiversity, environmental challenges, and knowledge shortfalls. We performed a Systematic Literature Review to analyze the seed dispersal network in the Cerrado and the potential impacts of the defaunation of large-bodied frugivores on it. We considered network metrics, calculated the defaunation index of the frugivore assemblage, and compared traits among different fruit-sized plants and their respective dispersers in the network. We retrieved 1,565 interactions involving 193 plant species and 270 animal species. Results show that the Cerrado seed dispersal network is slightly nested and considerably modular, dominated by small to medium-sized generalist species, such as passerines, marsupials, and mesocarnivores. Nonetheless, large-bodied frugivores like the lowland tapir have a key role in the network due to their great foraging and network integration capacity. The Cerrado frugivore assemblage is moderately defaunated, with possible effects in its interactions with large-fruited plants. The Cerrado's defaunation and functional loss of large vertebrates deserve urgent attention to further understand the impacts on seed dispersal mechanisms and ecosystem functioning.</span></p>
Dataset and network checkpoint for integrated microscope
<p>This file includes the blurry test images and sharp network output images as well as the pre-trained network weights in Large depth-of-field ultra-compact microscope by progressive optimization and deep learning.</p>
FIGURE 5. Haplotype network for Austroniscus brandtae n in Combining morphological and mitochondrial DNA data to describe a new species of Austroniscus Vanhöffen, 1914 (Isopoda, Janiroidea, Nannoniscidae) linking abyssal and hadal depths of the Puerto Rico Trench
FIGURE 5. Haplotype network for Austroniscus brandtae n. sp. for the mitochondrial ribosomal large subunit 16S. Sampled haplotypes are shown as solid circles with circle area proportional to the number of individuals possessing that haplotype; black circles represent unsampled haplotypes required to connect the network. The number of mutational steps between haplotypes are shown along connecting lines. The colours represent sampling locations as indicated in the legend.
FIGURE 4. Haplotype network for Austroniscus brandtae n in Combining morphological and mitochondrial DNA data to describe a new species of Austroniscus Vanhöffen, 1914 (Isopoda, Janiroidea, Nannoniscidae) linking abyssal and hadal depths of the Puerto Rico Trench
FIGURE 4. Haplotype network for Austroniscus brandtae n. sp. for COI (cytochrome c oxidase subunit I). Sampled haplotypes are shown as solid circles with circle area proportional to the number of individuals possessing that haplotype; black circles represent unsampled haplotypes required to connect the network. The number of mutational steps between haplotypes are shown along connecting lines. The colours represent sampling locations as indicated in the legend.
Rysunki dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains the figures for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Wyniki symulacji dla Communication and Computing Task Allocation for Energy-Efficient Fog Networks
<p>This resource contains simulation results for Communication and Computing Task Allocation for Energy-Efficient Fog Networks</p>
Raw data and R code for: Negative effects of urbanisation on diurnal and nocturnal pollen-transport networks
<p>Pollinating insects are declining due to habitat loss and climate change, and cities with limited habitat and floral resources may be particularly vulnerable. The effects of urban landscapes on pollination networks remain poorly understood, and comparative studies of taxa with divergent niches are lacking. Here, for the first time, we simultaneously compare nocturnal moth and diurnal bee pollen-transport networks using DNA metabarcoding and ask how pollination networks are affected by increasing urbanisation. Bees and moths exhibited substantial divergence in the communities of plants they interact with. Increasing urbanisation had comparable negative effects on pollen-transport networks of both taxa, with significant declines in pollen species richness. We show that moths are an important, but overlooked, component of urban pollen-transport networks for wild flowering plants, horticultural crops, and trees. Our findings highlight the need to include both bee and non-bee taxa when assessing the status of critical plant-insect interactions in urbanised landscapes.</p>
Mapping Monasticism: A Digital Approach to the Network of Conques (raw data)
<p>Original dataset used to produce the digital map of the Monastic Network of Conques. The file includes a link to the GMaps, as well as the KMZ file of the map itself.</p>
NYC Truck Network
<p>New York City road network with Truck Route preferences, speeds, and height restrictions </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.