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9,152 results for “Networking”
Tables of direct, indirect and networks estimates and Forests plots of pairwise comparisons for all secondary outcomes
<p>Supplementary File #12 for Cochrane review entitled: "Non-invasive respiratory support in preterm infants as primary mode: a network meta-analysis"</p>
Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein-protein interaction networks
<p>The datasets used to evaluate Enhanced Gaussian noise augmentation-based contrastive learning (EGsCL) against predicting the pro-longevity or anti-longevity effect of model organism gene. This repo also includes the pretrained encoders that obtained the best predictive performance for each organism (see Table 2).</p>
Contaminated genome assembly for decontamination training as part of the Galaxy Training Network
Open the record for dataset details and reuse information.
Data from Demonstration of quantum network protocols over a 14-km urban fiber link
<p>Datasets used to create all plots and results found in the paper "Data from Demonstration of quantum network protocols over a 14-km urban fiber link" from AG Eschner, Saarland University</p>
Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets - Dataset
<h1>Overview</h1> <p>This archive contains the data from a network latency measurement campaign conducted inside the wireless ISP JackRabbit Wireless between 2023-10-30 and 2023-12-01. The measurement setup and an analysis of the data is provided in the paper "Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets", accepted for the ACM Internet Measurement Conference 2024. </p> <p>Code for parsing the data and reproducing the plots can be found at <a href="https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git">https://github.com/simosund/Measuring-Network-Latency-from-a-Wireless-ISP.git</a></p> <h2>Manifest</h2> <ul> <li><strong>The raw subfolder:</strong> contains the original data captured by epping from the measurement campaign. However, note that we have merged all subnet entries from the internal network into a single entry at each timepoint to avoid revealing details about the subnet structure on the internal network. Some internal subnet entries that only carry control traffic have also been removed. It is still possible to recreate all the results from the paper with this data.</li> <li><strong>The processed subfolder: </strong>This contains processed versions of the data that have been merged in a few different ways, allowing the results from the paper to be recreated quickly without having to parse all of the original data. <ul> <li><strong>subnets_stats_merged.json:</strong> The data from the entire measurement period have been merged into a single entry per subnet</li> <li><strong>{lan,wan}_per10s_stats_merged.json:</strong> All the internal or external data merged into a single entry per timepoint (10s).</li> <li><strong>top_100asn_stats_10min_json:</strong> The external data merged per ASN (for the 100 ASNs with the highest amount of downlink traffic during the measurement period) at a 10 minute resolution.</li> <li><strong>protocol_counters.hdf5: </strong>Counters for number of packets of each <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml">IP-protocol</a>. The most common protocols, such as TCP, UDP and ICMP are named, where less common ones are just numbered.</li> <li><strong>ecn_counters.hdf5:</strong> Counters of the four ECN-marks (Not-ECT, ECT(1), ECT(0) and CE).</li> </ul> </li> <li><strong>The overhead_measurements subfolder: </strong>CPU and network load measurements from a later period used to estimate the overhead of the epping tool. In the covered period, epping was running between the 19th of April and the 3rd of May. <ul> <li><strong>InfluxDB_CPU_20240413-20240513.csv:</strong> CSV with hourly CPU utilization per core on the DuT.</li> <li><strong>jackrabbit-shaper-per\_day-20240421-20240513.csv:</strong> CSV with network load metrics (throughput in bytes and packets) every 48 minutes.</li> <li><strong>proglistoutput_after_enabling_bpf_stats_24hr.txt:</strong> Output from <code>bpftool prog list</code> after enabling <code>sysctl -w kernel.bpf_stats_enabled=1</code>, containing run counts and cumulative runtime for all eBPF programs on the system.</li> </ul> </li> <li><strong>rrc14_20231115_ipasn.dat: </strong>Data from RIPE´s route collector RRC14 in the middle of the measurement campaign (2023-11-15), used to map subnets into ASNs.</li> <li><strong>asn.txt:</strong> RIPE's list of AS names (from <a href="https://ftp.ripe.net/ripe/asnames/asn.txt">https://ftp.ripe.net/ripe/asnames/asn.txt</a>), used to translate ASNs into AS names.</li> </ul> <h2>Data format for raw data</h2> <p>The raw data is organized into one subfolder per day, with each subfolder containing one compressed json-file per minute. All files have been included in a tar-archive.</p> <p>The JSON format in each file consists of a single list of entries. There are three types of entries.</p> <h3>Configuration entry</h3> <p>Each file starts with one configuration entry, containing the following fields:<br><br></p> <ul> <li><strong> timestamp:</strong> Unix timestamp in nanoseconds when entry was created</li> <li><strong>bins: </strong>The maximum number of bins in the histogram of a subnet entry. Always 250 in this dataset. Note that histograms may contain less bins as unused bins in the upper range are truncated.</li> <li><strong>bin_width_ns:</strong> The width of the bins in nanoseconds. Always 4000000 (4ms) in this dataset.</li> <li><strong>aggregation_interval_ns: </strong>How often the aggregated stats are reported in nanoseconds. Always 10000000000 (10s) in this dataset.</li> <li><strong>timeout_interval_ns: </strong>How long epping will keep a subnet entry around since it last saw traffic for it. Always 30000000000 (30s) in this dataset. Note that a subnet entry may only be deleted after its stats are reported, so no stats will be lost regardless of value.</li> <li><strong>ipv4_prefix_len:</strong> Subnet size to aggregate IPv4 traffic. Always 24 (/24) in this dataset.</li> <li><strong>ipv6_prefix_len: </strong>Subnet size to aggregate IPv6 traffic. Always /48 in this dataset.</li> </ul> <p>Example:</p> <pre><code>{<span>"timestamp"</span>: <span>1698661062033443066</span>, <span>"bins"</span>: <span>250</span>, <span>"bin_width_ns"</span>: <span>4000000</span>, <span>"aggregation_interval_ns"</span>: <span>10000000000</span>, <span>"timeout_interval_ns"</span>: <span>30000000000</span>, <span>"ipv4_prefix_len"</span>: <span>24</span>, <span>"ipv6_prefix_len"</span>: <span>48</span>}</code></pre> <h3><br>Subnet stats entry</h3> <p>The most common entry will be the subnet stats entry, which contains the aggregated stats for one /24 subnet (or /48 if IPv6) over a 10 second period. Each entry always contains the following fields:</p> <ul> <li><strong>timestamp:</strong> A unix timestamp in nanoseconds of when the entry were reported. The entry will thus contain the stats for the period [timestamp - 10s, timestamp].</li> <li><strong>ip_prefix:</strong> The subnet in standard IPv4 or IPv6 CIDR notation.</li> <li><strong>rx_stats</strong> and <strong>tx_stats:</strong> Packet and byte counts for packets received from/transmitted to the subnet. Note that <em>rx</em> and <em>tx</em> is from the perspective of the epping node, not the perspective of the subnet, so <em>rx</em> is packets epping has seen sent from the subnet, and <em>tx</em> is packets epping has seen sent towards the subnet. <ul> <li><strong>TCP_TS</strong>, <strong>TCP_noTS</strong> and <strong>other:</strong> Entries for TCP traffic that has TCP timestamps enabled, TCP traffic with TCP timestamps disabled, and non-TCP traffic, respectively. The entry will be omitted if there is no traffic of the relevant type. <ul> <li><strong>packets:</strong> Number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header).</li> </ul> </li> </ul> </li> </ul> <p>Additionally, the following fields will be present if any RTT has been observed for the subnet. Note that the subnet will include RTTs for traffic going towards the subnet, i.e. SRC -> subnet -> SRC.</p> <ul> <li><strong>min_rtt:</strong> The minimum observed RTT in nanoseconds.</li> <li><strong>max_rtt:</strong> The maximum observed RTT in nanoseconds.</li> <li><strong>histogram:</strong>: A histogram with all observed RTTs. The bin width and maximum number of bins is specified by the configuration entry (always 4ms wide bins and max 250 bins in this dataset). Note that the histogram will truncate 0-bins after the last non-zero bin (e.g. if the maximum RTT is 7 ms, the histogram will only contain 2 bins).</li> <li><strong>count_rtt:</strong> The total number of RTTs observed (sum of histogram bin counts)</li> <li><strong>mean_rtt:</strong> The approximate mean RTT in nanoseconds.</li> <li><strong>median_rtt:</strong> The approximate median RTT in nanoseconds.</li> <li><strong>p95_rtt:</strong> The approximate 95th percentile of the RTTs in nanoseconds</li> </ul> <p>Note that <em>mean_rtt</em>, <em>median_rtt</em> and <em>p95_rtt</em> are all calculated from the histogram, assuming all RTTs are in the middle of the histogram bins, and will thus be +/- 2ms of the real value in this dataset.</p> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072800129847</span>, <span>"ip_prefix"</span>: <span><span>"</span>100.64.0.0/24<span>"</span></span>, <span>"rx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>64816</span>, <span>"bytes"</span>: <span>23063341</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>39783</span>, <span>"bytes"</span>: <span>16074723</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>8505</span>, <span>"bytes"</span>: <span>7403649</span>}}, <span>"tx_stats"</span>: {<span>"TCP_TS"</span>: {<span>"packets"</span>: <span>134424</span>, <span>"bytes"</span>: <span>175370347</span>}, <span>"other"</span>: {<span>"packets"</span>: <span>76267</span>, <span>"bytes"</span>: <span>86104300</span>}, <span>"TCP_noTS"</span>: {<span>"packets"</span>: <span>7946</span>, <span>"bytes"</span>: <span>5174210</span>}}, <span>"min_rtt"</span>: <span>50766</span>, <span>"max_rtt"</span>: <span>440315940</span>, <span>"histogram"</span>: [<span>622</span>, <span>596</span>, <span>835</span>, <span>1106</span>, <span>932</span>, <span>410</span>, <span>228</span>, <span>139</span>, <span>108</span>, <span>68</span>, <span>78</span>, <span>79</span>, <span>73</span>, <span>63</span>, <span>49</span>, <span>35</span>, <span>22</span>, <span>33</span>, <span>31</span>, <span>24</span>, <span>21</span>, <span>18</span>, <span>23</span>, <span>15</span>, <span>20</span>, <span>20</span>, <span>37</span>, <span>35</span>, <span>96</span>, <span>113</span>, <span>95</span>, <span>80</span>, <span>36</span>, <span>22</span>, <span>24</span>, <span>27</span>, <span>17</span>, <span>31</span>, <span>9</span>, <span>9</span>, <span>14</span>, <span>18</span>, <span>10</span>, <span>21</span>, <span>22</span>, <span>21</span>, <span>22</span>, <span>25</span>, <span>8</span>, <span>9</span>, <span>16</span>, <span>8</span>, <span>12</span>, <span>9</span>, <span>7</span>, <span>5</span>, <span>6</span>, <span>6</span>, <span>3</span>, <span>4</span>, <span>4</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>1</span>, <span>3</span>, <span>3</span>, <span>1</span>, <span>0</span>, <span>3</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>1</span>, <span>2</span>, <span>2</span>, <span>1</span>, <span>2</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>2</span>, <span>0</span>, <span>1</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>0</span>, <span>1</span>], <span>"count_rtt"</span>: <span>6568</span>, <span>"mean_rtt"</span>: <span>37529232.64311815</span>, <span>"median_rtt"</span>: <span>18000000.0</span>, <span>"p95_rtt"</span>: <span>150000000.0</span>}</code></pre> </div> <div> <h3>Global counters entry</h3> <p>Each report interval will also contain one entry with global counters, with the following fields:</p> <ul> <li><strong>timestamp:</strong> Unix timestamp in nanoseconds of when the entry was reported. The entry will thus contain stats for the period [timestamp - 10s, timestamp].</li> <li><strong>protocol_counters:</strong> A set of fields with packet and byte counters for the <a href="https://www.iana.org/assignments/protocol-numbers/protocol-numbers.xhtml" rel="nofollow">protocols indicated in the IP-header</a>. The most common protocols will be named, specifically <strong>TCP</strong>, <strong>UDP</strong>, <strong>ICMP</strong>, <strong>ICMPv6</strong> and the special <strong>non-IP</strong> for non-IP packets. All other protocols will simply use the protocol number. Protocols for which no traffic has been observed are omitted. <ul> <li><strong>packets:</strong> The number of packets of the specific type</li> <li><strong>bytes:</strong> Sum of bytes in the observed packets, including headers (up to and including the 14-byte Ethernet header). Only the list of named protocols above will include this byte counter.</li> </ul> </li> <li><strong>ecn_counters:</strong> Counters for the four distinct ECN code points in the IP header. May contain the fields <strong>no_ECT</strong> (00), <strong>ECT1</strong> (01), <strong>ECT0</strong> (10) and <strong>CE</strong> (11).</li> </ul> </div> <p>Example:</p> <div> <pre><code>{<span>"timestamp"</span>: <span>1698661072848798575</span>, <span>"protocol_counters"</span>: {<span>"TCP"</span>: {<span>"packets"</span>: <span>249032</span>, <span>"bytes"</span>: <span>242282230</span>}, <span>"UDP"</span>: {<span>"packets"</span>: <span>119778</span>, <span>"bytes"</span>: <span>108251262</span>}, <span>"ICMP"</span>: {<span>"packets"</span>: <span>1523</span>, <span>"bytes"</span>: <span>445347</span>}, <span>"ICMPv6"</span>: {<span>"packets"</span>: <span>87</span>, <span>"bytes"</span>: <span>25031</span>}, <span>"41"</span>: {<span>"packets"</span>: <span>1</span>}, <span>"47"</span>: {<span>"packets"</span>: <span>251</span>}, <span>"89"</span>: {<span>"packets"</span>: <span>22</span>}}, <span>"ecn_counters"</span>: {<span>"no_ECT"</span>: <span>347980</span>, <span>"ECT1"</span>: <span>2</span>, <span>"ECT0"</span>: <span>22709</span>, <span>"CE"</span>: <span>3</span>}, <span>"errors"</span>: {}}</code></pre> </div>
Fourier-neural-networks
<p>This library contains the data set used in the article ' Fast geophysical forward modeling simulation using transfer learning in the Electromagnetic method ' and the code of the Fourier neural network. The Fourier Neural Operator for 2D problems such as the Darcy Flow is discussed in Section 5.2 in the [paper](https://arxiv.org/pdf/2010.08895.pdf). And the original code can be downloaded from <a href="https://github.com/neuraloperator/neuraloperator">neuraloperator/neuraloperator: Learning in infinite dimension with neural operators. (github.com).</a></p>
Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures
<p>This is the training data for the paper "Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures"</p>
Dataset for the article "A deep equivariant neural network approach for efficient hybrid density functional calculations"
<p>Dataset files for DeepH-hybrid, containing structures and preprocessed Hamiltonian matrices computed with HSE06 hybrid functional and ABACUS DFT package.</p> <p>Enclosed include:</p> <ol> <li>The four datasets corresponding to the four DeepH-hybrid models mentioned in DeepH-hybrid's paper, each containing material structures and electronic structure properties of </li> <li>Basis set files for the ABACUS DFT package</li> <li>Snapshot of a version of the additional codes utilized to preprocess hybrid DFT Hamiltonians (also available via this <a href="https://github.com/aaaashanghai/DeepH-hybrid">GitHub link</a>)</li> </ol> <p>Note only the additional codes of DeepH-hybrid is provided, and it may be used in combination with DeepH-E3 package for neural-network training. For additional details please refer to the "README" file of the code.</p>
FIGURE 3. Haplotypes network from rpl32 in Molecular and cytogenetic confirmation of the hybrid origin of Jacobaea ×mirabilis (Asteraceae, Senecioneae), with nomenclatural notes on this name
FIGURE 3. Haplotypes network from rpl32-trnL region in Jacobaea. Each circle corresponds to a haplotype and circles' size is proportional to haplotype frequency (from n=1 to n=5). Small white circles represent single mutational steps.
FIGURE 3. TCS haplotype network obtained for 521 in Mitochondrial DNA-based reassessment of Antennablennius Fowler (Blenniidae: Salariini) from the north-western Indian Ocean, with resurrection of A. persicus (Regan)
FIGURE 3. TCS haplotype network obtained for 521 bp fragment of mitochondrial COI of the genus Antennablennius. Numbers between haplotypes represent mutational steps between them.
FIGURE 1. Phylogenetic relationship between Metarhizium synnematis GZUHXCHL12 in Delimitation of a novel member of genus Metarhizium (Clavicipitaceae) by phylogenetic and network analysis
FIGURE 1. Phylogenetic relationship between Metarhizium synnematis GZUHXCHL12 and its allies based on ITS-5.8S rDNA sequence data. Bootstrap values (1,000 replicates) are indicated above the nodes.
FIGURE 3 in Delimitation of a novel member of genus Metarhizium (Clavicipitaceae) by phylogenetic and network analysis
FIGURE 3. Metarhizium synnematis (holotype) A. Synnema on a lepidopteran cocoon. B. Upper part of a synnema. C. Phialides covering the surface of the synnema in a hymenium with conidia aggregating in sticky masses. D. Phialides. E. Conidia. Scale bars: A = 10 mm, B = 100 μm, C–E = 10 μm.
FIGURE 2 in Delimitation of a novel member of genus Metarhizium (Clavicipitaceae) by phylogenetic and network analysis
FIGURE 2. Minimum spanning network displaying the relationship among Metarhizium synnematis, Neotyphodium sp., Akanthomyces sp. and Metarhizium sp.
FIGURE 3. Haplotype network showing relationships among ITS haplotypes between Lepra amaroides, L. pseudosubventosa and L. subventosa s in New species and records of lichens from Bolivia
FIGURE 3. Haplotype network showing relationships among ITS haplotypes between Lepra amaroides, L. pseudosubventosa and L. subventosa s.str. Sizes of circles are proportional to the number of specimens per haplotype. Chemotypes are described below specimen's data. Numbers in brackets near lines between haplotypes represent number of mutational steps.
FIGURE 4. TCS haplotype network inferred from ITS-2 in The polyphasic approach revealed new species of Chloroidium (Trebouxiophyceae, Chlorophyta)
FIGURE 4. TCS haplotype network inferred from ITS-2 rDNA sequences of Chloroidium saccharophilum. This network was inferred using the algorithm described by Clement et al. (2002). Sequence nodes corresponding to samples collected from different geographical region and from different habitats.
FIGURE 5. TCS haplotype network inferred from ITS-2 in The polyphasic approach revealed new species of Chloroidium (Trebouxiophyceae, Chlorophyta)
FIGURE 5. TCS haplotype network inferred from ITS-2 rDNA sequences of Chloroidium ellipsoideum and C. lichenum. This network was inferred using the algorithm described by Clement et al. (2002). Sequence nodes corresponding to samples collected from different geographical region and from different habitats.
Latent space active learning with message passing neural network: the case of HfO_{2}
<div>A dataset containing the structures obatined from the active learning process and their calculated energies and forces. See more details in our papr "Latent space active learning with message passing neural network: the case of HfO_{2}".</div>
The data for "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework"
<p>The data is for supporting our research paper titled "Noise-Resilient Forward Transient Analysis for Pipe Burst Localization in Complex Pipe Networks: Theoretical Framework".</p>
Raw data for Jin et. al "Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks"
<h2><strong><span>Intersectin1 promotes clathrin-mediated endocytosis by organizing and stabilizing endocytic protein interaction networks</span></strong></h2> <p><span> </span></p> <p><span>Meiyan Jin<sup>1,2*</sup>, Yuichiro Iwamoto<sup>1</sup>, Cyna Shirazinejad<sup>1</sup>, David G. Drubin<sup>1,3**</sup></span></p> <p><sup><span>1 </span></sup><span>Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720, USA</span></p> <p><sup><span>2</span></sup><span> Current Address: Department of Biology, University of Florida, Gainesville, Fl 32611, USA</span></p> <p><sup><span>3</span></sup><span> Lead author</span></p> <p><sup><span>* </span></sup><span>Correspondence: meiyan.jin@ufl.edu</span></p> <p><sup><span>**</span></sup><span>Correspondence: </span><span>drubin@berkeley.edu</span></p>
Efficacy and safety of different monoclonal antibodies for osteoarthritis: a Bayesian network meta-analysis
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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.