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9,152 results for “Networking”
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
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multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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Figure 1 in Lefkada temporary network
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SNR and RSSI from an urban LoRa Network
<p>The dataset contains various SNR and RSSI reading from a LoRa Network in the city of Portland, Maine. The dataset consists in a single sheet file, where each row corresponds to a specific LoRa node. Note that: i) the first row corresponds to the LoRa Gateway; ii) each row contains information over the node ID, its coordinates, the packet frequency, bandwidth, , Transmission Power, Spreading Factor, Coding Rate, SNR and RSSI.</p>
Supplementary data and code for "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes"
<p>This repository contains the relevant data and code supporting the study "A network toxicology approach for mechanistic modelling of nanomaterial hazard and adverse outcomes". Files are password protected during the revision process. A completely public version of the repository will be availble after the revision process is completed. </p> <p>In detail, the uploaded archive folder contains the following data sources:</p> <ul> <li>the relevant code and supporting data (code_to_upload and supporting_data);</li> <li>supplementary materials of the paper, including: <ul> <li>individual enrichment results of the 93 exposures to the 31 ENMs (enrichments_results);</li> <li>comparison between the mechanism of action retrieved from differentially expressed genes and network modelling (network_comparison_results);</li> <li>overrepresented network edges in categories of networks (overrepresented_structures)</li> </ul> </li> </ul>
Consistency and differences between centrality measures across distinct classes of networks
<p>This is the data reported in Oldham et al., 2019. Consistency and differences between centrality measures across distinct classes of networks (<a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0220061">link to paper</a>). Originally hosted <a href="https://figshare.com/s/22c5b72b574351d03edf">here</a>.</p> <p>GitHub for the code used to generate and use this data is located <a href="https://github.com/StuartJO/CentralityConsistency">here</a>. Unzip the zip file into the main directory into a folder called "Data".</p>
Metadata - Direct C−H Arylation of Dithiophene-Tetrathiafulvalene: Tuneable Electronic Properties and 2D Self-Assembled Molecular Networks at the Solid/Liquid Interface
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Dataset and codes: Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <ul> <li>Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz files readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE)</li> <li>Animated gif files of Pt1 migration between double-six rings in CHA, Pt3 jump through an eight-ring in CHA, and insertion of Pt1 into a t-pen unit in MFI</li> <li>Neural Network Potential (NNP) files readable by <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> </ul>
Fine-grained classification of journal articles by relying on multiple layers of information through similarity network fusion: the case of the Cambridge Journal of Economics
<p>prova</p>
Common Network Database
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Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity
<p><strong>Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity '</strong><br><strong>Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.nbd.2023.106382</span></a></strong></p> <p><strong>Dataset description</strong></p> <p><em>Data\data2publish\sub- </em>: 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs,<em> </em>Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal </p> <p><em>Data\data2publish\EA_info.xlsx</em>: number of epileptiform activities automatically detected for each animal at d28 and d29</p> <p><em>Data\data2publish\derivatives\eeg_preprocessing: </em>results of the script A_EEG_preprocessing.m, for each animal and session</p> <p><em>Data\data2publish\derivatives\elec_layout: </em>different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4</p> <p><em>Data\data2publish\derivatives\network_metrics</em>_<em>wpli: </em>results of network analyses (script C_network_analyses.m)</p> <p><em>Data\data2publish\derivatives\wpli</em>: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit</p> <p><strong>Code for analyses available here: </strong> <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a></p> <p>Abbreviations:</p> <p>EEG= electroencephalography</p>
Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"
<p>Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow</p>
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks: dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p>The associated codes are available in the repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></p>
Data from: Research on potential disruptive technology identification based on technology network
<p><span>Three evident and meaningful characteristics of disruptive technology are the zeroing effect that causes sustaining technology useless for its remarkable and unprecedented progress, reshaping the landscape of technology and economy, and leading the future mainstream of technology system, all of which have profound impacts and positive influences. The identification of disruptive technology is a universally difficult task. Therefore, the paper aims to enhance the technical relevance of potential disruptive technology identification results and improve the granularity and effectiveness of potential disruptive technology identification topics. According to the life cycle theory, dividing the time stage, then constructing and analyzing the dynamic of technology networks to identify potential disruptive technology. Thereby, using the LDA topic model further to clarify the topic content of potential disruptive technologies. This paper takes the large civil UAVs as an example to prove the feasibility and effectiveness of the model. The results show that the potential disruptive technology in this field is the main equipment, data acquisition, and information transmission.</span></p>
Neural network kinetics
<p>The folder contains the data for creating main figures and supplementary figures in the original paper.</p>
Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks
<p>The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data. </p> <p>In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.</p> <p> </p>
Women's empowerment and the Social Networking Site
<p>The data in the Dataset is organized into two groups: the first refers to the sociodemographic data of the interviewees and the second contains the questions asked of the interviewees. In this second group, the original questions are in Brazilian Portuguese and next to them is a version translated into British English.</p> <p><strong>Group 1:</strong></p> <p><strong>ID Interview: </strong>This column serves as a unique identifier for each interview, facilitating the organization and referencing of individual responses.</p> <p><strong>Age Range: </strong>Categorizes participants into specific age groups, providing insights into the distribution of respondents across different age demographics.</p> <p><strong>Marital Status: </strong>Records the marital status of participants, offering information on their relationship status.</p> <p><strong>Where Do You Live: </strong>Captures the geographical location or residence of each participant, providing a snapshot of the diverse locations represented in the study.</p> <p><strong>Number of Sons: </strong>Reflects participants' family composition by recording the number of sons they have.</p> <p><strong>Academic Background: </strong>Outlines the educational qualifications of participants, offering insights into their level of education and expertise.</p> <p><strong>Current Position Held: </strong>Highlights the current job roles or positions held by participants, providing an overview of the variety of professions within the sample.</p> <p><strong>Length of Time in Position: </strong>Records the duration for which participants have held their current positions, offering insights into their professional stability or career progression.</p> <p><strong>The Organization's Branch of Activity: </strong>Categorizes the type of industry or sector in which participants' organizations operate, offering insights into the diversity of business sectors.</p> <p><strong>Scope of the Organization's Operations: </strong>Delves into the extent and scale of the organization's activities, providing information on whether it operates locally, nationally, or internationally.</p> <p><strong>Number of Employees: </strong>Indicates the size of the organizations represented, offering insights into the range from small businesses to large corporations.</p> <p><strong>Monthly Income: </strong>Records the monthly income of participants, providing insights into the financial aspects of the respondents.</p> <p><strong>Group 2:</strong></p> <p>1. O que é empoderamento feminino para você? 1. What is women's empowerment to you? 2. Você acredita que seja uma mulher empoderada? Por quê? 2. Do you think you're an empowered woman? Why? 3. Como os sites de redes sociais podem contribuir para o processo de capacitação das mulheres? 3. How can social networking sites contribute to the process of women's empowerment? 4. E como elas (as mídias sociais) podem dificultar isso (o empoderamento das mulheres)? 4. And how can they (social medias) make it (women's emporwement) difficult? 5. Em sua opinião, o compartilhamento de conteúdo no grupo "Mulheres de negócios" ou o uso da ferramenta "Escrever um artigo" no LinkedIn pode influenciar/incentivar as mulheres de sua rede social a discutir o empoderamento das mulheres? Por quê? 5. In your opinion, can sharing content in the "Business Women" group or using the "Write an Article" tool on LinkedIn influence/encourage the women in your social network to discuss women's empowerment? Why? 6. Em sua opinião, como os sites de redes sociais profissionais, como o LinkedIn, podem ajudar as mulheres no mercado de trabalho? 6. In your opinion, how can professional social networking sites such as LinkedIn help women in the job market? 7. E como os grupos fechados do LinkedIn, como o "Mulheres de Negócios", podem ajudar? 7. And how can LinkedIn's closed groups, such as "Women in Business", help? 8. Em sua opinião, os sites de redes sociais profissionais, como o LinkedIn, prejudicam de alguma forma a carreira das mulheres no mercado de trabalho? 8. In your opinion, do professional social networking sites such as LinkedIn hinder women's careers in the job market in any way? 9. Você acha que, ao usar o LinkedIn para divulgar seu perfil profissional, as mulheres podem ter acesso a vagas de emprego que não encontrariam na maneira convencional de procurar/oferecer empregos? Comente. 9. Do you think that by using LinkedIn to publicize their professional profile, women can gain access to job vacancies that they wouldn't find in the conventional way of looking for/offering jobs? Comment. 10. Você chegou ao topo de sua carreira? Comente. 10. Have you reached the top of your career ladder? Comment. 11. Como você reconhece, na prática, os possíveis limites de sua carreira profissional? 11. How do you recognize, in practice, the possible limits of your professional career?</p> <p> </p>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
Fig. 4. Minimum spanning network and mismatch distribution from cyt b in Echinoderes galadrielae Grzelak & Sørensen 2022, sp. nov.
Fig. 4. Minimum spanning network and mismatch distribution from cyt b DNA sequences of Rhodeus ocellatus in each lineage. The colors correspond to the lineage in figure 2. The size of each circle represents the number of specimens of each haplotype. The small, open circle between different haplotypes represents the number of mutations. The number of mutations> 8 is shown as the mutation number beside the traverse lines. The results of mismatch distribution are shown in the histograms. The abscissa and ordinate of the histograms indicate the number of pairwise difference between specimens and the frequency of each value, respectively. The black and gray bars represent the frequency distribution of the observed and expected pairwise difference respectively under the sudden expansion model.
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.