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1,721 results for “network data”
Botnet Data - Computer Networks Lab 2022/2
<p>Dataset created for the Computer Network Laboratory class at UFES.</p> <p>Reproduction in a different experimental setup of the botnet attack presented in:</p> <p>"An empirical comparison of botnet detection methods" Sebastian Garcia, Martin Grill, Jan Stiborek and Alejandro Zunino. Computers and Security Journal, Elsevier. 2014. Vol 45, pp 100-123. http://dx.doi.org/10.1016/j.cose.2014.05.011</p> <p>More specifically, the scenario https://mcfp.felk.cvut.cz/publicDatasets/CTU-Malware-Capture-Botnet-48/</p> <p>For more information, https://github.com/VitorSpa/LabRedes-2023</p> <p> </p>
Data used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'
<p>The dataset contains the CML, OTT disdrometer, rain gauge and radar data as well as some of the scripts used in the paper titled 'Reconstructing and Nowcasting the Rainfall Field by a CML Network'</p>
Data: Upsamling Monte Carlo Neutron Transport Simulation Tallies Using a Convolutional Neural Network
<p>This repository contains:</p> <ul> <li>openmc-data-XXXX.tar.gz - Training Data generated with the OpenMC Monte Carlo code representing neutron flux tallies in 4,400 unique light water reactor fuel assemblies in HDF5 format. Training samples consist of tallies in 64x64 pixels and 8 neutron energy groups, and tallies in 128x128 pixels and 16 neutron energy groups. Folders 0008 to 0023 contain training and validation data. Folder 0024 contains test data.</li> <li>out.mat - Upsampling results using a Convolutional Neural Network for 300 testing data samples in MATLAB format. These data include OpenMC tally uncertainties in low and high resolution tallies, scaling values used in data pre-processing, low resolution inputs to the CNN, and high resolution upsampled results as well as high resolution ground truth values.</li> </ul>
Artificial neural network model and metabolomics data of selected microbial strains
<p>Metabolomics data, metadata, sample R code, and a pre-trained artificial neural network model to predict group memberships of the bacterial strains in the dataset.</p>
Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data
<p>Data for the preprint "Double shrinking (DOSH), a regression-based algorithm for gene regulatory network inference from co-expression data". The preprint is live on ResearchSquare: <a href="http://t.researchsquare.com/track/click/31114617/doi.org?p=eyJzIjoiWVJQQUYtT09mMXFoWnRoMGk0SlpQZTZqWWpJIiwidiI6MSwicCI6IntcInVcIjozMTExNDYxNyxcInZcIjoxLFwidXJsXCI6XCJodHRwczpcXFwvXFxcL2RvaS5vcmdcXFwvMTAuMjEyMDNcXFwvcnMuMy5ycy0yNzM4NjgzXFxcL3YxXCIsXCJpZFwiOlwiZDMzODNjZGNhNWNiNGE2Yjk5NWRkY2UyNmIyODI5NTlcIixcInVybF9pZHNcIjpbXCIzZGQwZTAxMmExMzk4NDhkNTAzYjI4ZTBiZmU1Y2QxMDcxNzhlZTgwXCJdfSJ9">10.21203/rs.3.rs-2738683/v1</a>.</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>
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>
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.
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>
Application of deep neural networks to reconstruct coastal water quality data
<p>In this study ordinary and new integrated deep neural networks were developed for the reconstruction of measured specific conductance (<em>SC</em>) data. Five stations of USGS in the Gulf of Mexico were considered as case study.</p>
Data for "Monte Carlo samplers for efficient network inference"
<p>This directory contains data corresponding to all figures in the publication "Monte Carlo samplers for efficient network inference" in PLOS Computational Biology by Z. Kilic et al.</p>
Data for Objective identification of pressure wave events from networks of 1-Hz, high-precision sensors
<p>Data from pressure sensors assembled by Matthew Miller which consist of either a Bosch BMP388 or Bosch BME280 Adafruit breakout board connected to a Raspberry Pi Zero W single-board computer used to log the data. Data are recorded at 1-second intervals. The data are stored in .csv files: one for each day for each sensor. Sensors were placed in networks in the Toronto, ON, Canada, New York, NY, USA, and Raleigh, NC, USA metro areas. Code for processing these data can be found at https://doi.org/10.5281/zenodo.8087843.</p>
Data for "A multinode quantum network over a metropolitan area"
<p>This dataset is for the research article "A multinode quantum network over a metropolitan area".</p>
Data and Codes of "A Pharmacological Representation-based LSTM Network for Drug–Drug Interaction Extraction"
<p>The datasets and source codes used and/or analyzed in study "A Pharmacological Representation-based LSTM Network for Drug–Drug Interaction Extraction".</p>
Supplementary code and data for "Tackling Universal Properties of Minimal Trap Spaces of Boolean Networks"
<p>Supplementary code and data for the conference article</p> <blockquote> <p>"Tackling universal properties of minimal trap spaces of boolean networks" Sara Riva, Jean-Marie Lagniez, Gustavo Magaña López, and Loïc Paulevé Proceedings of CMSB 2023, LCNS, Springer.<a href="https://doi.org/10.1007/978-3-031-42697-1_11"> https://doi.org/10.1007/978-3-031-42697-1_11</a></p> </blockquote> <p>Requirements</p> <ul> <li> <p>Python at least 3.9</p> </li> <li> <p><a href="https://github.com/bnediction/bonesis">bonesis</a> (v0.5.6) - <a href="https://doi.org/10.5281/zenodo.7984628">https://doi.org/10.5281/zenodo.7984628</a></p> <p>With pip:</p> <ul> <li><code>pip install bonesis==0.5.6</code></li> </ul> <p>With conda:</p> <ul> <li><code>conda install -c potassco -c colomoto bonesis=0.5.6</code></li> </ul> </li> <li> <p>for the synthesis: <a href="https://github.com/sybila/biodivine-aeon-py/">AEON.py</a></p> <p>With pip:</p> <ul> <li><a href="https://rustup.rs">https://rustup.rs</a></li> <li><code>pip install biodivine-aeon</code></li> </ul> <p>With conda</p> <ul> <li><code>conda install -c daemontus biodivine_aeon</code></li> </ul> </li> </ul> <p> </p> <p>Usage</p> <ul> <li> to execute the CEGAR approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_cegar.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>The first parameter is the .bnet file, the second one is the marker (in JSON), and the third is the maximum number k of components in a perturbation. To fix the marker, it is also possible to use a special node (ex: p) in the bnet file and require a marker '{"p":1}'. In the Moon22 dataset, information about uncontrollable components are available, one can use <code>--exclude '["Apoptosis","Growth_Arrest","Proliferation"]'</code>. If the marker is given as a component, it must be involed in the uncontrollable list.</p> <p>Moreover, the markers of the Moon22 dataset are contained in the bnet files in a special component <code>p</code>. Concerning the Trappist dataset, the possible markers are contained in files .markers, the user can choose between them.</p> <p>The dataset synthetic_bns contains random generated BNs. Also in this case, different possible markers are contained in files .markers.</p> <ul> <li>to execute the Complementary approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_complementary.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the Enumeration & Filtering approach for marker-reprogramming problem:</li> </ul> <pre><code>python reprogramming_trapspaces_naive.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.bnet '{"p":1}' 3 </code></pre> <p>Again, uncontrollable components can be specified.</p> <ul> <li>to execute the CEGAR approach for a synthesis problem:</li> </ul> <pre><code>python synthesis.py instances/Moon22/L3_2013_Grieco_et_al/transition_formula.aeon '{"p":1}' </code></pre> <p>The first parameter is the prior knowledge graph in an .aeon file and the second one is the marker (in JSON). Another option is <code>--maxclause</code> which allow one to specify the maximum integer number of clauses authorised in local functions (by default is 128). The option <code>--no-canonic</code> leads to specify the criteria for the refinement of the under-approximation (3 for TS(y)|=M ; 1 for \exists y s.t. TS(y)!=TS(x) or 0 to obtain just a different candidate solution).</p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Data for three power distribution systems are presented in this document. The first system consists of 53 nodes and 61 branches. The second is composed of 217 nodes and 219 branches. Finally, the third system consists of 404 nodes and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Data for "Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity"
<p>The trained 600-member ensemble convolutional neural networks (CNNs) for seasonal tropical cyclone (TC) activity to allow future studies. Please refer Fu et al. (2023; <em>Using Convolutional Neural Network to Emulate Seasonal Tropical Cyclone Activity</em>) for more details.</p>
Data for articles in "Historical Network Analysis in the Study of Chinese Religion" (Special issue Religions 2023)
<p>This is the data for the seven articles collected in the special issue of <i>Religions</i> (2023) "Historical Network Analysis in the Study of Chinese Religion":</p><p> - Bingenheimer, Marcus. 2023. "Miyun Yuanwu 密雲圓悟 (1567–1642) and His Impact on 17th-Century Buddhism" Religions 14, no. 2: 248. https://doi.org/10.3390/rel14020248</p><p>- Chen, Song. 2023. "Patterns of Integration: A Network Perspective on Popular Religious Connections in China's Lower Yangzi, 1150–1350" Religions 14, no. 5: 577. https://doi.org/10.3390/rel14050577</p><p>- Chu, Ming-Kin. 2023. "Realizing the "Outwardly Regal" Vision in the Midst of Political Inactivity: A Study of the Epistolary Networks of Li Gang 李綱 (1083–1140) and Sun Di 孫覿 (1081–1169)" Religions 14, no. 3: 389. https://doi.org/10.3390/rel14030389</p><p>- Goossaert, Vincent. 2023. "The Social Networks of Gods in Late Imperial Spirit-Writing Altars" Religions 14, no. 2: 217. https://doi.org/10.3390/rel14020217</p><p>- Nehrdich, Sebastian. 2023. "Observations on the Intertextuality of Selected Abhidharma Texts Preserved in Chinese Translation" Religions 14, no. 7: 911. https://doi.org/10.3390/rel14070911</p><p>- Sokolova, Anna. 2023. "Regional Buddhist Communities in Tang China and Their Social Networks: The Network of Master Fayun (?–766)" Religions 14, no. 3: 335. https://doi.org/10.3390/rel14030335</p><p>- Van Cutsem, Laurent. 2023. "Lineages as Network: A Study of Chan Genealogy in the Zutang ji 祖堂集 Using Social Network Analysis" Religions 14, no. 2: 205. https://doi.org/10.3390/rel14020205</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.