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9,152 results for “Networking”
Fig. 8 Haplotype network for 44 in New insights into the phylogeny and taxonomy of Chinese species of Gagea (Liliaceae)-speciation through hybridization
Fig. 8 Haplotype network for 44 cpDNA haplotypes (psbA-trnH IGS+trnL-trnF IGS) including 38 sequences of representatives of Gagea sect. Gagea: G. aipetriensis (aip), G. ancestralis, G. angelae (ang), G. artemczukii (art), G. capusii (cap), G. erubescens (eru), G. helenae (hel), G. huochengensis (huo), G. lutea (lut), G. nakaiana (nak), G. paczoskii (pac), G. podolica (pod), G. pomeranica (pom), G. pratensis (pra), G. pusilla (pus), G. rubicunda (rub), G. shmakoviana (shm), G. terraccianoana (ter), G. tisoniana (tis), G.
Fig. 4 Haplotype networks for ITS2 in Cryptic species of Notophyllum (Polychaeta: Phyllodocidae) in Scandinavian waters
Fig. 4 Haplotype networks for ITS2. (A, B) Notophyllum foliosum. (C, D) N. crypticum n. sp. For further explanations, see Figure 3 10 4 5 3 8 9 B T 6 7 2 T 1 B
Fig. 3 Haplotypic networks for the studied histone H1 in Estimating range disjunction time of the Palearctic Admirals (Limenitis L.) with COI and histone H1 genes
Fig. 3 Haplotypic networks for the studied histone H1 gene (a–c) and COI fragment (d) of L. camilla (a–b) and L. helmanni (c–d) from the eastern and western parts of their ranges; b shows representation of combinations of histone H1 gene haplotypic variants in individuals of L. camilla
Precise Tuning of Regional Hydrological LSTM Networks
<div> <div>This data is for paper: "Precise Tuning of Regional Hydrological LSTM Networks: Simultaneous Systematic Random Search Optimization" by F. Hosseini et al. (Preprint)</div> <div>"<strong>Hyperparameter Optimization of Regional Hydrological LSTMs by Random Search: A Case Study from Basque Country, Spain</strong>" (Accepted on 29th Aug 2024) J.Hydro, Hosseini, et al., 2024.</div> <div> </div> <div>Codes to run and reproduce the results and figures can be found here: https://github.com/farzadhoseini/Precise_Tuning_of_Regional_Hydrological_LSTM_Networks</div> </div>
Exposing TCP watermarks in the Tor network using deep learning
<p><strong>Description</strong></p> <p>The datasets consist of Tor network flows that have been captured with packet analyser Wireshark and converted into CSV format. The network flows consist of file transfers of images whose size varies from a few kilobytes to several megabytes. The captured packets are flows from the entry guard of the connection to the client. The datasets contain both clean Tor traffic and "watermarked" Tor traffic. Watermarking is a method of leaving small prints on the network flow at the sender end and trying to detect them at the receiving end. A positive detection indicates a connection between the two parties, thus breaking the anonymity aspect of the Tor. </p> <p>The used watermarking algorithms are "Interval-based watermarking" (IBW) presented by Pyun et al. [1] in 2007 and "Scalable watermark that is invisible and resilient to packet losses" (SWIRL) presented by Houmansadr and Borisov [2] in 2011. The algorithms were implemented with a watermarking module, which is essentially a modified TCP/IP stack. The module is publicly available [3].</p> <p> </p> <p>The IBW-watermarked data is produced in the following way:</p> <p>The watermarked training data is endoded with the bit string {0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1} by introducing the following repeating delays (milliseconds):</p> <p>{0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0}.</p> <p>The watermarked test data is encoded with the bit string {0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0} by introducing the following repeating delays (milliseconds):</p> <p>{0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,0,1000,0,0,0,0, 1000,0,0,0,0, 1000,0,0,0,0, 0,0,0,0,1000,0,0,0,0, 0,0,0,0,1000,0,0,0,0}.</p> <p> </p> <p>The SWIRL-watermarked data is produced in the following way:</p> <p>The watermarked training data is encoded with a repeating delay string (milliseconds) {100, 0, 250, 250, 100, 250, 250, 100, 0, 0, 250, 0, 0, 100, 250, 0, 0, 250, 0, 250}, which mimics a SWIRL's permutation. </p> <p>The watermarked test data is encoded with a repeating delay string (milliseconds) {100, 100, 300, 300, 300, 100, 300, 100, 0, 0, 100, 100, 0, 0, 300, 0, 100, 100, 300, 0}, which also mimics a SWIRL's permutation. </p> <p> </p> <p>The datasets have been collected as a part of master's thesis work at Tampere University and are used primarily in neural network classification tests. The datasets intended for neural network training are longer and have "_train.csv" endings in their names. Datasets intended for testing are shorter and have "_test.csv" endings in their names. </p> <p> </p> <p><strong>References</strong></p> <p>[1] Y. J. Pyun, Y. H. Park, X. Wang, D. S. Reeves and P. Ning. "Tracing Traffic through Intermediate Hosts that Repacketize Flows," <em>IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications</em>, Anchorage, AK, USA, 2007</p> <p>[2] A. Houmansadr and N. Borisov. “SWIRL: A Scalable Watermark to Detect Correlated Network Flows,” <em>Network and Distributed System Security Symposium, </em>San Diego, United States, 2011</p> <p>[3] https://gitlab.com/nisec/tcp-watermark/</p>
US Ozone Network (1980-2017)
<p># Data Repository Structure</p> <p>The dataset is organized into state-specific folders, identified by the EPA state code for each U.S. state. Each state folder is structured into three primary subfolders as described below:</p> <p>## Folder Organization</p> <p>Each state folder contains the following subfolders:</p> <p>### 1. Correlations/o3<br>This subfolder houses yearly directories from 1980 to 2017. Each year-specific directory includes:<br>- `Correlations.xlsx`: An Excel spreadsheet containing correlation data.<br>- `nodeNames.pickle`: A serialized Python pickle file storing node names used in correlation analyses.<br>- `correlations.pickle`: A serialized Python pickle file containing correlation matrices.</p> <p>### 2. CorrelationsSummary<br>- Contains summarized network data for each year from 1980 to 2017.</p> <p>### 3. Data/o3<br>- Includes pickled data files, storing data in a serialized format suitable for quick loading in Python environments.</p> <p>## Usage</p> <p>Each dataset is prepared for analysis and can be loaded using Python's `pickle` module or accessed via Excel for the `.xlsx` files. Ensure that your environment supports these formats to utilize the data effectively.</p> <p>## Contributing</p> <p>Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.</p> <p>## License</p> <p>This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.</p> <p>## Contact</p> <p>For any queries or further information, please contact harshit@cs.toronto.edu(mailto:harshit@cs.toronto.edu).</p>
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
Fig. 3. A–B, Pyrops candelaria. A in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 3. A–B, Pyrops candelaria. A, Chambok, 1.IX.2014 (S. Phauk). B, Kampong Tralach, 21.IX.2013 (O. Rodriguez). C–D, P. coelestinus, Chambok, 1.IX.2014 (S. Phauk). E, P. condorinus, Koh Kong, Tatai, 24.V.2015 (G. Chartier). F, P. ducalis, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). G–I, P. peguensis. G, Tumpor, Cardamom, 12.VIII.2009 (J. Holden). H, Chambok, 5.V.2015 (J. Constant). I, idem, biotope. J, P. spinolae, Ratanakiri, Veun Sai Siem Pang, 23.II.2015 (Marduk). K, P. viridirostris, Chambok, 7.V.2015 (J. Constant). L–N, Saiva gemmata. L, nymph, Chambok, 7 May 2015 (J. Constant). M, adult tended by a cockroach, Chambok, 7.V.2015 (J. Constant). N, Mondulkiri, Seima Forest, 13.V.2015 (B. Barca). O–Q, Zanna sp. O–P, Koh Kong, Tatai, 1.XI.2012 (G. Chartier). Q, Kampot, 21.XII.2013 (K.W. Meier-Doernberg).
One-Way Wave Propagator driven by pure visual neural network
<p>this repository is used to reproduce the key Figures of our manuscript titled "One-Way Wave Propagator driven by pure visual neural network". </p> <p>The code is running using MATLAB, Recommend using a GPU device.</p>
Instances and results of the paper "Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance"
Open the record for dataset details and reuse information.
Fig. 2 Median joining network for the seven cytochrome b in Ecomorphology of a generalist freshwater gastropod: complex relations of shell morphology, habitat, and fecundity
Fig. 2 Median joining network for the seven cytochrome b haplotypes found in 979 New Zealand Potamopyrgus antipodarum and sampling sites. Each branch represents a single nucleotide substitution and short transversal lines as well as small black circles unsampled haplotypes. Size of circles is proportional to number of individuals per haplotype
Fig. 9 Single gene trees and haplotype networks. a, b in Two new bioluminescent Henlea from Siberia and lack of molecular support for Hepatogaster (Annelida, Clitellata, Enchytraeidae)
Fig. 9 Single gene trees and haplotype networks. a, b Gene-trees (a COI, b H3) estimated with Bayesian coalescent analysis in BEAST. Numbers above branches are posterior probabilities. Scales show expected numbers of substitution per site. The specimen shaded in red has a conflict between morphology and COI data. c, d Statistical parsimony
Dataset of "Detecting Weaknesses and Analyzing Cascading Failures in Critical Infrastructure Networks Using an Interconnected Simulation System"
<p>This article introduces an innovative simulator for critical infrastructure, developed to enable comprehensive<br>simulation of data and power grid and their interconnections. The simulator offers user-input functionality,<br>allowing for detailed modelling and analysis of various impact scenarios on infrastructures. A key feature<br>of this tool is the use of a state matrix to represent the current state of infrastructures, which is updated<br>after each simulation. This update facilitates accurate and dynamic modelling of changes in infrastructure<br>networks and their interconnections.<br>The simulator is designed to test the functionality of both individual components and the overall network<br>integrity, including the analysis of the cascading failures, where a failure in one part can impact other parts<br>of the infrastructure. This capability is essential for a deeper understanding of risks and for developing<br>effective strategies to protect and ensure the resilience of critical infrastructures. The simulator represents a<br>significant advancement in the field of critical infrastructure simulation, providing a tool for better prediction,<br>identification, and mitigation of potential threats, thereby enhancing the security and resilience of critical<br>systems.</p>
A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms
<p>The dataset for "A Graph Neural Network Based Workflow for Real-time Lightning Location with Continuous Waveforms" can be divided into training and validation sets at any desired ratio.</p> <p> </p> <p>The code has been published on GitHub: <a href="https://github.com/cqtian-kk/Lightning_Detection_Location">Lightning_Detection_Location</a> or <a href="https://zenodo.org/records/14048427">DOI 10.5281/zenodo.13350849</a></p>
Reproduction Package for Submission `A Literature Review on Verification and Abstraction of Neural Networks'
<div> <div>This artifact contains the aggregated data for the article "A Literature Review on Verification and Abstraction of Neural Networks".</div> <br> <div>The artifact contains the following data files:</div> <br> <div>|-- phase-1_search-space.csv</div> <div>|-- phase-2-3_process-title-abstract.csv</div> <div>|-- review_sheet.csv</div> <br> <h3>Search space for literature review</h3> <div>The file <code>phase-1_search-space.csv</code> contains the metadata of the papers published in our considered conferences during the observed years.</div> <br> <h3>Filtering process</h3> <div>In the file <code>phase-2-3_process-title-abstract.csv</code>, we include the files that contain our expected keywords.</div> <div>Depending on whether we decided to include the paper in our survey after reading the title and the abstract, we marked each of the papers with `accept` or `decline`.</div> <br> <h3>Review sheet</h3> <div>The file <code>review_sheet.csv</code> contains metadata describing our reviews for each of the publications.</div> <div>It holds information on our final review of each paper, including the group we assigned it to and the reasons for its exclusion.</div> <br> <h3>Generating numbers</h3> <div>The following commands can be executed to reproduce the numbers used in our literature review.</div> <br> <div><code># Change directory to the directory containing the CSV files of this artifact.</code></div> <br> <div><code># Number of papers in our search space.</code></div> <div><code>cat phase-1_search-space.csv | tail -n +2 | wc -l</code></div> <br> <div><code># Number of papers after keyword search.</code></div> <div><code>cat phase-2-3_process-title-abstract.csv | tail -n +2 | wc -l</code></div> <br> <div><code># Number of papers that are excluded based on abstracts and titles.</code></div> <div><code>cut -f9 phase-2-3_process-title-abstract.csv | tail -n +2 | sort | uniq -c</code></div> <br> <div><code># Number of papers in different classes.</code></div> <div><code>cut -f7 review_sheet.csv | tail -n +2 | sort | uniq -c</code></div> <div> </div> </div>
Supplementary data for the article: "Towards an widely applicable earthquake detection algorithm for fibreoptic and hybrid fibreoptic-seismometer networks"
<p>Repository of supporting data associated with the article submitted to GJI, titled: "Towards an widely applicable earthquake detection algorithm for fibreoptic and hybrid fibreoptic-seismometer networks".</p> <p> </p> <p>Contents include:</p> <ol> <li>A full working example of modified QuakeMigrate version and seismic time-series data used to detect an earthquake.</li> <li>Earthquake catalogues for the three datasets described in the publication.</li> </ol>
Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks"
<p>Data and code for "Design Principles for Energy Transfer in the Photosystem II Supercomplex from Kinetic Transition Networks".</p>
Supplementary data (CC BY-NC-SA 4.0): Germanium Distributions in Zeolites Derived from Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <p>This dataset contains supplementary data related to the Germanosilicate Project titled <br>"Germanium Distributions in Zeolites Derived from Neural Network Potentials". <br>In various subfolders, it hosts database, simulations and post-processing calculations.<br><br>Below is a brief overview of each subfolder:</p> <ol> <li><em>Post_Processing_Calculation:</em> <br>- Contains scripts and data for post-processing calculations such as coordination numbers, <br>- Pair distribution function, and various germanium distribution metrics.<br><br></li> <li><em>NNP_Simulation_DATA:</em> <br>- Stores simulation data for different zeolite structures along with setup files for neural network potentials (NNP) simulations.<br><br></li> <li><em>NNP_files_database</em>: <br>- NNP_files: NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)<br>- GeSiO_training.db: DFT (PBE+D3(BJ)) training database as SchNetPack1.0 database file readable by <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment (ASE)</a> and SchNetPack version 1.0<br><br><em> </em></li> <li><em>DFT_vs_NNP_Data:</em> <br>- ASE traj files storing structures subsampled from MCBH runs along with energies/forces at the PBE+D3(BJ) ("*_dft.traj") and NNP level (*_nnp.traj)<br><br></li> <li><em>Zeolite_Structurers_ALL</em>: <br>- Holds data for various zeolite structures, including optimized structures for both single-cell and supercell configurations.<br><br></li> <li>GSOs_DATA:<br>- The unoptimised Global Structure Optimas (GSOs) are provided<br>- Optimised GSOs are stored inside folders for both various DFT and NNP methods</li> </ol> <p> <br>Please refer to individual readme files in each subfolder for more detailed information.</p>
Data Release: "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function"
<p>This dataset contains results presented in "<strong>A neural network emulator of the Advanced LIGO and Advanced Virgo selection function</strong>" (<a href="https://www.arxiv.org/abs/2408.16828">arXiv: 2408.16828</a>).</p> <p>The code used to generate this data and produce figures in the paper can be found at <a href="https://github.com/tcallister/learning-p-det/">https://github.com/tcallister/learning-p-det/</a>. Specific instructions about the workflow are provided in the <a href="https://tcallister.github.io/learning-p-det/">accompanying documentation</a>.</p> <p>The primary deliverable of this work is a trained neural network emulator for the compact binary selection function during the Advanced LIGO and Advanced Virgo O3 observing run. This emulator is made available in a standalone companion repository, <a href="https://github.com/tcallister/pdet">https://github.com/tcallister/pdet</a>.</p> <p>Additional information:</p> <ul> <li>The files <em>endo3_bbhpop-LIGO-T2100113-v12.hdf5</em>, <em>endo3_bnspop-LIGO-T2100113-v12.hdf5</em>, and <em>endo3_nsbhpop-LIGO-T2100113-v12.hdf5</em>, used for network training, were created and released by the LIGO-Virgo-KAGRA Collaboration at <a href="../records/7890437">https://zenodo.org/records/7890437</a>.</li> <li>The file <em>sampleDict_FAR_1_in_1_yr.pickle</em>, used during hierarchical inference, was created via code in the repository <a href="https://github.com/tcallister/get-lvk-data">https://github.com/tcallister/get-lvk-data</a>.</li> <li>Inference results (<em>popsummary_standardInjections.h5</em> and <em>popsummary_dynamicInjections.h5</em>) are provided in the <em>popsummary</em> results format; see <a href="https://git.ligo.org/christian.adamcewicz/popsummary">https://git.ligo.org/christian.adamcewicz/popsummary</a>.</li> </ul> <p>Changelog:</p> <ul> <li>v2: Added missing file <em>sampleDict_FAR_1_in_1_yr.pickle</em></li> </ul>
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.