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9,152 results for “Networking”
Educational transformation and network learning dataset – qualitative data from an international collaborative EU-project
<p>We are releasing our dataset of workshop outcomes acquired from the annual consortium conferences organized by the international “NextFood” consortium. The purpose of this project is to develop new ways of educating the future sustainability leaders of the agrifood sector, making sure that the professionals (farmers, advisers, businesses, students) have the right set of skills and competences needed to tackle the sustainability challenges we face ahead. Data gathering started from May 2018 yielding considerable amount of data on achievements, challenges and action plans related to educational transformation. This dataset will be updated by the time of project finalization. This work was funded by the European Union, through the Horizon 2020 project “NextFood”, Grant agreement No. 771738.</p>
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>WARNING! </strong>This dataset is no longer updated after January 2022. Refer to the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> with different license terms for an up to date version.</p> <p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>WARNING:</strong>This dataset is now longer updated after January 2022. The original dataset is still updated.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> <a href="https://doi.org/10.5194/essd-13-357-2021">https://doi.org/10.5194/essd-13-357-2021</a></p> <p> </p>
Fungal Network Analysis - Dataset
<p>Images of fungal mycelia from basidiomycetes, ascomycetes and zygomycetes with associated parameter files for processing using the Fungal Network Analysis software available from DOI: <a href="https://doi.org/10.5281/zenodo.5187932">10.5281/zenodo.5187932</a>.</p> <p>Please cite:</p> <p>Aguilar-Trigueros, C.A., Boddy, L., Rillig, M.C. and Fricker, M.D. (2022) Network traits predict ecological strategies in fungi. <em>ISME Communications.</em> <strong>2, </strong>2</p> <p>https://doi.org/10.1038/s43705-021-00085-1</p>
MeshDapp: Blockchain-enabled Payment System for Wireless Mesh Networks - Dataset
<p>The MeshDapp dataset includes data from the MeshDapp project - Fed4Fire+ Open Call 9 winner:</p> <p>Data includes: Network Measurement from the CityLab - Antwerp testbed and IRIS (TCD) testbed. </p> <p>MeshDapp Paper:https://mvdsi.seeu.edu.mk/mselimi/papers/DAPPS20ms.pdf </p>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Figure - Network Organisation
<p>Figure "Network Organisation" created for the MIDA Project application document in 2018. </p>
Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)
<p>The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>).</p> <p>The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3. </p> <p>All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line. The interconnected system is fully represented in "Network_XXX.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_XXX);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected.. The generated orderbook is presented in "OrderbookTN_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
Polifonia Ontology Network, v0.1
<p>The Polifonia Ontology Network, a set of OWL ontology modules to describe the content and context of tangible and intangible musical cultural heritage assets from Europe.</p> <p>Included modules are: <a href="https://github.com/polifonia-project/core/">Core</a>, <a href="https://github.com/polifonia-project/musical-performance/">Musical Performance</a>, <a href="https://github.com/polifonia-project/musical-composition/">Musical Composition</a>, <a href="https://github.com/polifonia-project/musical-feature/">Musical Feature</a>, <a href="https://github.com/polifonia-project/source">Source</a>, <a href="https://github.com/polifonia-project/instrument">Instrument</a>, <a href="https://github.com/polifonia-project/comparative-measure">Comparative Measure</a>, <a href="https://github.com/polifonia-project/music-emotion">Music Emotion</a>, <a href="https://github.com/polifonia-project/metadata">Metadata</a>, <a href="https://github.com/polifonia-project/bell">Bell</a></p>
A Deep Neural Network Based SMAP Soil Moisture Product
<p>The soil moisture datasets here are based on a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The bias is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables include polarized SMAP brightness temperatures, incidence angles, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
Defeating Adversarial Attacks Againt Adversarial attacks in Network Security
<p>We investigate if the feature randomization approach to improve the robustness of forensic detectors to targeted attacks in network security, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering, two original network architectures and different classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.</p>
Data Set for the Journal Article "Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis"
<p>This dataset includes the XYZ structures of the centroids of all compounds found. Charge and multiplicity are given in the comment line of each XYZ file.</p>
Datasets and results of the paper titled "Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"
<p>These are the <strong>input datasets</strong> and the <strong>results of the analyses</strong> reported on the paper titled <strong>"Are citation networks relevant to explain academic promotions? An empirical analysis of the Italian national scientific qualification"</strong>.</p> <p><strong>Abstract:</strong> </p> <p>The aim of this paper is to study the role of citation network measures in the assessment of scientific maturity. Referring to the case of the Italian national scientific qualification (ASN), we investigate if there is a relationship between citation network indices and the results of the researchers’ evaluation procedures. In particular, we want to understand if network measures can enhance the prediction accuracy of the results of the evaluation procedures beyond basic performance indices. Moreover, we want to highlight which citation network indices prove to be more relevant in explaining the ASN results, and if quantitative indices used in the citation-based disciplines assessment can replace the citation network measures in non-citation-based disciplines. Data concerning Statistics and Computer Science disciplines are collected from different sources (ASN, Italian Ministry of University and Research, and Scopus) and processed in order to calculate the citation-based measures used in this study. Following, we apply classification models to estimate the effects of network variables. We find that network measures are strongly related to the results of the ASN and significantly improve the explanatory power of the models, especially for the research fields of Statistics. Additionally, citation networks in the specific sub-disciplines are far more relevant than those in the general disciplines. Finally, results show that the citation network measures are not a substitute of the citation-based bibliometric indices.</p> <p><strong>Code</strong></p> <p>The code to collect and process the data used in this paper is available on GitHub at <a href="https://github.com/DigitalDataLab/ASN16-18_CitationNetwork">https://github.com/DigitalDataLab/ASN16-18_CitationNetwork</a><strong>.</strong> </p> <p><strong>Dataset description</strong></p> <p>The files <strong>AdjacencyMatrix_01B1.csv</strong>, <strong>AdjacencyMatrix_09H1.csv</strong>, <strong>AdjacencyMatrix_13D1.csv</strong>, <strong>AdjacencyMatrix_13D2.csv</strong> and <strong>AdjacencyMatrix_13D3.csv</strong> are the citation matrices for Italian academics (i.e. ASN candidates and permanent positions in the Italian academic system) in the Recruitment Fields (RFs) 01/B1, 09/H1, 13/D1, 13/D2 and 13/D3, respectively.</p> <p>The files <strong>AdjacencyMatrix_CS.csv</strong> and <strong>AdjacencyMatrix_ST.csv</strong> are the citation matrices for the Italian academics in the Computer Science disciplines (i.e. RFs 01/B1 and 09/H1) and the Statistical disciplines (i.e. RFs 13/D1, 13/D2 and 13/D3), respectively.</p> <p>The files <strong>CS_01B1_1.csv, CS_09H1_1.csv, ST_13D1_1.csv, ST_13D2_1.csv</strong> and <strong>ST_13D3_1.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Full Professor (FP) level.</p> <p>The files <strong>CS_01B1_2.csv, CS_09H1_2.csv, ST_13D1_2.csv, ST_13D2_2.csv</strong> and <strong>ST_13D3_2.csv</strong> contain the data used to build the logistic regression models presented in the paper for the Italian academics at the Associate Professor (AP) level.</p> <p>The file <strong>Codebook.pdf</strong> is the codebook of the previous ten files.</p> <p>The file <strong>Appendix.pdf</strong> contains the final results of the stepwise logistic regressions computed for each level (i.e. Full Professor and Associate Professor) and Recruitment Field in the Computer Science and Statistics disciplines.</p> <p>The file <strong>NormalityAssessment.pdf</strong> contains the normality assessment of citation network indices. </p>
Language intensity classification and Neuronal Networks
<p>Kohonen Self-Organizing Maps (SOM) are a particular type of artificial neural network created by Teuvo Kohonen. Their unsupervised learning makes them suitable for application, among other things, to grouping tasks. Occupations have been grouped into this analysis considering the similarity in value of the variables that define this occupation in terms of language proficiency requirements. The 8 variables used were: v1-speaking skills, v2-writing skills, v3-speech clarity, v4-speech recognition, v5-English knowledge, v6-speaking ability, v7-communication with people outsiders, v8-communication with superiors, equals or subordinates. After applying this authomatic classification technique, the SOC occupations are classified according to five distinc groups with decreasing linguistic intensity:</p> <p>Class 1: High linguistic intenisty requirements<br> Class 2: Medium-high linguistic intenisty requirements<br> Class 3: Medium linguistic intenisty requirements<br> Class 4: Medium-low linguistic intenisty requirements<br> Class 5: Low linguistic intenisty requirements</p>
Generated Data for the Manuscript "Nonideality-Aware Training for Accurate and Robust Low-Power Memristive Neural Networks"
<p>The file contains data generated and referred to in the text and the figures of the manuscript.</p>
Control Network for Ganymede Images from JunoCam Perijove 34
<p>Delivered is a photogrammetric control network for four images of Ganymede collected by the Juno- Cam instrument during the Juno mission’s perijove 34 encounter on 2021-06-07. The network has 184,659 control points and 369,318 control measures (2 measures per control point). The network is a PVL formatted text file for use in ISIS (Integrated Software for Imagers and Spectrometers). When control points are merged with an image distance tolerance of 1.0 pixel the resulting network has 83,096 control points and 233,598 control measures. Extracting control points with 4 or more measures yields a network with 16,709 control points and 75,197 control measures.</p> <p>The initial network is produced with novel code implemented in Mathematica.</p> <p>In the “Initial Network” 98% of residuals are less than 1.72 pixels. Median residual is .40 pixels. There are 54 control points with residuals greater than 3 pixels, and 1544 with greater than 2.</p> <p>See ControlNetworkJunoCamV16j.pdf for details.</p>
A dataset of published journal papers using neural networks for seismological tasks.
<p>This is a dataset of 637 journal papers applying neural networks for various tasks in seismology spanning January 1988 to January 2022. The dataset mainly includes peer reviewed papers and does not contain duplicated works. It follows a hierarchical classification of papers based on seismological tasks (i.e. category, sub_category_I, sub_category_II, task, and sub_task). For each paper following information are provided: 1) first author's last name, 2) publication year, 3) paper's title, 4) journal 's name, 5) machine learning method used, 6) the type of used neural network, 7) the name of neural network architecture, 8) the number of neurons/kernels in each hidden layer, 9) type of training process, i.e. supervised, semi-supervised, etc, 10) input data into the network, 11) output data, 12) data domain, i.e. time, frequency, feature, etc, 13) the type of data used for training, e.g. synthetic or real data, 14) the size of training set, 15) the metrics used to measure the performance, 16) performance scores, 17) the baseline method used for evaluation, and 18) a short note summarizing the paper's objective, its approach, and its significance. </p> <p>An updating version of the dataset can be find from here: https://smousavi05.github.io/dl_seismology/ and here:https://github.com/smousavi05/dl_seismology/tree/main/docs. </p> <p>An updating glossary of seismological tasks and relevant machine learning techniques and papers are provided here: https://smousavi05.gitbook.io/mlseismology/</p>
Illustrations of a Diels Alder network
<p>This dataset contains three illustrations of a Diels-Alder self-healing polymer, made using Blender.</p> <p>The network is shown in 3 different thermal states. </p>
Local survey network and monitoring data of VLBI telescope at Metsähovi
<p>Observations of local surveying network and monitorin at Metsähovi. More information is in file Data_description.pdf.</p>
Graph Theoretical Measures of Fast Ripple Networks Support the Epileptic Network Hypothesis
<p>MongoDB JSON files of the (high-frequency oscillation) HFO and electrode databases used for this study and others.</p>
HIKARI-2021: Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
<p>Available datasets from the paper Generating Encrypted Network Traffic for Intrusion Detection Datasets.</p> <p>To produce the dataset follow the technical detail in <a href="https://github.com/andreysfc/generating-encrypted-network">github</a></p>
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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.