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1,721 results for “network data”
Data from: Network analysis by simulated annealing of taxa and islands of Macaronesia (North Atlantic Ocean)
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Data from: Molecular analysis reveals high compartmentalisation in aphid-primary parasitoid networks and low parasitoid sharing between crop and non-crop habitats.
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Data from: Differential effects of fertilisers on pollination and parasitoid interaction networks
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Data and code from: Deep reinforcement learning for pressure optimization in water distribution networks with multiple pumping stations: Case study
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Data from: Widespread dysregulation of long non-coding genes associated with fatty acid metabolism, cell division, and immune response gene networks in xenobiotic-exposed rat liver
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Data for: Infectious disease and sickness behaviour: tumour progression affects interaction patterns and social network structure in wild Tasmanian devils
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Data from: Ongoing habenular activity is driven by forebrain networks and modulated by olfactory stimuli
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Data from: Ecosystem engineers shape ecological network structure and stability: a framework and literature review
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Data from: Research on potential disruptive technology identification based on technology network
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Data from: A stochastic generative model for citation networks among academic papers
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Data from: The structure of the Mini-K and K-SF-42: a psychological network approach
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Training Data of Quantitative Online NMR Spectroscopy for Artificial Neural Networks
<p>Data set of low-field NMR spectra of continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). <sup>1</sup>H spectra (43 MHz) were recorded as single scans.</p> <p> Two different approaches for the generation of artificial neural networks training data for the prediction of reactant concentrations were used: (<em>i</em>) Training data based on combinations of measured pure component spectra and (<em>ii</em>) Training data based on a spectral model.</p> <p><strong>Synthetic low-field NMR spectra</strong></p> <p>First 4 columns in MAT-files represent component areas of each reactant within the synthetic mixture spectrum.</p> <p><em>X<sub>i</sub></em> (“pure component spectra dataset”)</p> <p><em>X<sub>ii</sub></em> (“spectral model dataset”)</p> <p><strong>Experimental low-field NMR spectra from MNDPA-Synthesis</strong></p> <p>This data set represents low-field NMR-spectra recorded during continuous synthesis of nitro-4’-methyldiphenylamine (MNDPA). Reference values from high-field NMR results are included.</p>
Reconciliation of regulatory data: the regulatory networks of Escherichia coli and Bacillus subtilis
<p>The dataset hereby uploaded, presents state-of-art, reconciled transcriptional regulatory networks of <em>Escherichia coli </em>K12 MG1655<em> </em>and <em>Bacillus subtilis </em>str 168<em>. </em>The networks were reconciled through the retrieval and integration of relevant regulatory data from multiple resources, including databases, such as <em>RegulonDB </em>and <em>DBTBS</em> as well as available literature.</p>
Data from: The co-authorship networks of the most productive European researchers
<p>This individual-level data-set describes the most productive European Union (EU) researchers (in terms of articles), during 2007 - 2018, irrespective of their research field. Specifically, in the data-set file, i.e. "iconic_5000", we profile the most productive 4,588 EU researchers using the following variables: number of papers; number of citations; repeated collaborations; number of co-authors; number of co-authors from the same country (as the author), from the same city, from the same institution and from different countries; geographical dispersion (number of unique countries wherein co-authors are based in), star (the largest number of articles published by one of an author's collaborators), godfather (the largest number of citations received by one of an author's collaborators), co-authors' citations and co-authors' papers. Variables are yearly measured.</p>
Data from: Amyloid and cerebrovascular burden divergently influence brain functional network changes over time
Objective: To examine the effects of baseline Alzheimer's disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connectivity (FC) changes in mild cognitive impairment (MCI) patients. Methods: We studied 30 amnestic (aMCI) and 55 subcortical vascular MCI (svMCI) patients with baseline Pittsburgh Compound B (PiB)–positron emission tomography (PET) scans and longitudinal magnetic resonance imaging (MRI) scans. Participants were followed up clinically with annual MR imaging for up to four years (aMCI: 26 with two time-points, 4 with three time-points; svMCI: 13 with two time-points, 16 with three time-points, 26 with four time-points). Results: Amyloid-β burden was associated with longitudinal DMN FC declines, while cerebrovascular burden was associated with longitudinal ECN FC changes. When patients were divided into PiB+ and PiB- groups, PiB+ patients showed longitudinal DMN FC declines, while svMCI patients showed longitudinal ECN FC increases. Direct comparisons between the two groups without mixed pathology (aMCI PiB+ and svMCI PiB-) recapitulated this divergent pattern: aMCI PiB+ patients showed steeper longitudinal DMN FC declines, while svMCI PiB- patients showed steeper longitudinal ECN FC increases. Finally, using baseline PiB uptake and lacune numbers as continuous variables, baseline PiB uptake showed inverse U-shape associations with longitudinal DMN FC changes in both MCI subtypes, while baseline lacune numbers showed both mainly inverse-U shape relationships with longitudinal ECN FC changes in svMCI patients. Conclusions: Our findings underscore the divergent effects of amyloid-β and cerebrovascular burden on longitudinal FC changes in the DMN and ECN in the pre-dementia stage, which reflect the underlying pathology and may be used to track early changes in Alzheimer's disease and cerebrovascular disease.
Data set for Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation
<p>The dataset contains over-the-air measurements on a 64 active antenna array (Anokiwave AWMF-0129) operating at 28 GHz carrier frequency and transmitting a 200 MHz OFDM waveform with FFT size of 4096, 3168 active subcarriers, subcarrier spacing of 60 kHz and 5 times oversampling w.r.t the critical sampling rate. The dataset contains the I/Q samples of the TX waveform as well as the corresponding over-the-air received signals when the electrical beam is steered toward different beamforming directions. This dataset allows to observe and study the so-called beam-dependent load modulation, which is the phenomenon that causes the nonlinear characteristics of the antenna array to change with the beamforming direction. A Matlab script for data visualization is also provided.</p> <p>For further details please refer to the following papers:</p> <p>A. Brihuega <em>et al</em>., "Piecewise Digital Predistortion for mmWave Active Antenna Arrays: Algorithms and Measurements," in <em>IEEE Transactions on Microwave Theory and Techniques</em>, doi: 10.1109/TMTT.2020.2994311.</p> <p>A. Brihuega <em>et al</em>., “Neural-Network-Based Digital Predistortion for Active Antenna Arrays Under Load Modulation,” in <em>IEEE Microwave and Wireless Components Letters, </em>doi: 10.1109/LMWC.2020.3004003</p>
Data used for "Studying human-nature relationships through a network lens: A systematic review"
<p>This is the data set used in the systematic literature review presented in Kluger et al. (<em>accepted for publicatio</em>n) Studying human-nature relationships through a network lens: A systematic review. <em>People and Nature</em>.</p> <p>The sheet "Full_analysis" includes all papers (n=138) identified - and classified relevant - through the literature search in the webofknowledge data base. For being classified as relevant, the study had to apply a network analytical approach and deal with human-nature relationships.</p> <p>For further details on the review process and decision criteria, please see the method section of the main manuscript.</p>
Bitcoin Network Transaction Inv Data with Java Timestamp and Originator Id
<p>The data is a gz compression of a csv file with the following layout:</p> <p><timestamp in ms>,<originator id as sha-256 hash in hexadecimal string representation>, <hash of BTC Transaction in hexadecimal string representation></p> <p>Example lines:</p> <p>1547779473468,6cf1100aaccec75da23995512fc7c7a5b6e25224f5903af011e78691c03d0455,a73578820a41aa6180621bcd90af1997c88794b33d8db2f004ee37c3e09b10ec<br> 1547779473468,6cf1100aaccec75da23995512fc7c7a5b6e25224f5903af011e78691c03d0455,0beb35a4338b026fb4ceb0d215d372f67dfb0c73f1e21157e03dde2fbc6b94ee</p> <p>The data was gathered with a modified version of bitcoinj 0.14.7. The modification consisted of:</p> <ul> <li>Blinding Originator IP+Port by hashing with a key generated at application startup</li> <li>Logging every Transaction Inv message to a file</li> </ul> <p>The application running the modified library just consisted of creating a Peer Pool of at most 6000 connections and limiting broadcasting to require 100000 connections, set via bitcoinj parameters.</p> <p>The data of crawler-82GB.gz was gathered between 17.01.2019, 15:30 and 18.01.2019, 03:00 at Ulm University in Ulm, Germany. The data of crawler-01-02-2019-09-54-59-ULM.csv.gz was gathered on 01.02.2019 at Ulm Iniversity in Ulm, Germany as well.</p> <p>The data of crawler-24.01.2019-*.gz was gathered between 24.01.2019, 10:00 and 25.01.2019 10:00 on Microsoft Azure instances. The selected region of Azure is indicated in the filename: UK, South; US, East; Southeast Asia.</p> <p>The datasets of crawler-06.02.2019-*-SIMUL.csv.gz were gathered with the same runtime key so ID's can be linked within the datasets. Locations again are Ulm University, Germany, and Azure UK, South (GBS); US, East (USA); Southeast Asia (SEA).</p> <p>ETH-crawler-20+21.02.2019.csv contains a similar collection of the Ethereum network for comparisson.</p>
Supporting data for "Synthesis and Simulation of Ensembles of Boolean Networks for Cell Fate Decision" by Chevalier et al., 2020
<p>Code, data, and notebooks used for the synthesis and simulations of ensembles of Boolean networks for the tumor invasion model introduced in <a href="https://doi.org/10.1371/journal.pcbi.1004571">(Cohen et al, 2015)</a></p> <p>Visualize online:</p> <ul> <li><a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Simulations%20-%20Mutant%20analysis.ipynb">Simulations - Mutant analysis.ipynb </a></li> <li> <a href="https://nbviewer.jupyter.org/urls/zenodo.org/record/3938904/files/Tumour%20-%20Synthesis%20with%20BoNesis.ipynb">Tumour - Synthesis with BoNesis.ipynb</a></li> </ul> <p>The notebooks can be executed within the <a href="http://colomoto.org/notebook">CoLoMoTo Docker</a> image 2020-07-01:</p> <pre><code>pip install -U colomoto-docker colomoto-docker -V 2020-07-01 --bind . </code></pre> <p>The synthesis additionally requires executing the following command (within the Docker image):</p> <pre><code>pip install --user bonesis-preview-20200514.zip </code></pre> <p>The ensembles have been generated with the following commands.</p> <pre><code>python synthesis.py synthesis --exact-pkn --globalfps python synthesis.py synthesis --exact-pkn --globalfps --mutant p53 --mutant NICD </code></pre> <p> </p> <ul> </ul>
Network origin-destination data
<p>The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Column B and C.</p> <p>Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.</p>
ScienceDex guides
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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.