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9,152 results for “Networking”
Soil nutrient analysis:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function.
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
Datasets for Age, Gender, and International Author Networks in the Earth and Space Sciences: Implications for Addressing Implicit Bias
<p>These files provide supplemental information and tabular data for figures in Hanson, Wooden, Lerback: Age, Gender, and International Author Networks in the Earth and Space Sciences: Implications for Addressing Implicit Bias, Submitted to Earth and Space Science.</p>
DeepLabCut network for pupillometry analysis (Privitera et al. 2020)
<p>Network trained for Privitera et al. 2020. Used for the analysis of pupillometry video files using an infrared camera and anesthetized mice</p>
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>
Result dataset of the PhD thesis "Enabling interoperability between MAC-heterogeneous sensor networks"
<p>This is a zip file containing the dataset of the results presented in the PhD thesis "Enabling interoperability between MAC-heterogeneous sensor networks". The archive is organised per chapter and contains both the "raw" data obtained for the individual tests and the "aggregated" data that is calculated from the "raw" data and used to generate the graphs in the thesis.</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>
The role of the Arp2/3 complex in shaping the dynamics and structures of branched actomyosin networks
<p>Actomyosin networks give cells the ability to move and divide. These networks contract and expand while being driven by active energy-consuming processes such as motor protein walking and actin polymerization. Actin dynamics is also regulated by actin-binding proteins, such as the actin-related protein 2/3 (Arp2/3) complex. This complex generates branched filaments thereby changing the overall organization of the network. In this work, the spatiotemporal patterns of dynamical actin assembly accompanying the branching-induced reorganization caused by Arp2/3 were studied using a computational model (MEDYAN); this model simulates actomyosin network dynamics as a result of chemical reactions whose rates are modulated by rapid mechanical equilibration. We show that branched actomyosin networks relax significantly more slowly than do unbranched networks. Also, branched networks undergo rare convulsive movements, “avalanches”, that release strain in the network. These avalanches are associated with the more heterogeneous distribution of mechanically-linked filaments displayed by branched networks. These far-from equilibrium events arising from the marginal stability of growing actomyosin networks provide a possible mechanism of the “cytoquakes” recently seen in experiments.</p>
Modeling plate and spring reverberation using a DSP-informed deep neural network
<p>Accompanying audio samples for the paper:</p> <p>Martínez Ramírez M. A., Benetos, E. and Reiss J. D., “Modeling plate and spring reverberation using a DSP-informed deep neural network” in the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020.</p> <p>Dry and wet bass and guitar recordings.</p> <p>Bass and Guitar dry notes are taken from the IDMT-SMT-Audio-Effects dataset. Author: Michael Stein (Fraunhofer IDMT) https://www.idmt.fraunhofer.de/en/business_units/m2d/smt/audio_effects.html</p> <p>Plate Reverb - Bass - recordings are taken from the IDMT-SMT-Audio-Effects dataset. Plate settings are the following:</p> <ul> <li><strong>Smaertelectronix ambience</strong>: ’Gating Amount - 0’, ’Gating Attack" - 10 ms’, ’Gating Release - 10 ms’, ’Decay Time - 2225 ms’, ’Decay Diffusion - 50%’, ’Decay Hold - off’, ’Shape Size - 16%’, ’Shape Predelay - 0 ms’, ’Shape Width - 100%’, ’Shape Quality - 100%’, ’Shape Variation - 0’, ’EQ Bass Frequency - 43 Hz’, ’EQ Bass Gain - −7.8 dB’, ’EQ Treble Frequency - 5044 Hz’, ’EQ Treble Gain - −3.7 dB’, ’Damping Bass Frequency - 158 Hz’, ’Damping Bass Amount - 87%’, ’Damping Treble Frequency - 8127 Hz’, ’Damping Treble Amount - 32%’, ’Dry - −Inf’, ’Wet - 0dB’.</li> </ul> <p>Spring Reverb - Bass and Guitar - recorded from the spring reverb tank<strong>: Accutronics </strong><strong>4</strong><strong>EB</strong><strong>2</strong><strong>C</strong><strong>1</strong><strong>B</strong>: ’Dry Mix - 0%’, ’Wet Mix - 100%’</p> <p>Plate<em> </em>reverb samples correspond to a VST audio plug-in, while spring<em> </em>reverb samples are recorded using an analog reverb tank which is based on 2 springs placed in parallel.</p> <p>The recordings are downsampled to 16 kHz. Also, since the plate reverb samples have a fade-out applied in the last 0.5 seconds of the recordings, we process the spring reverb samples accordingly.</p>
Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer
<p>This repository contains clinical data of LUAD samples (phnotype.csv; TCGA-LUAD.survival.tsv), microRNA targets data of RNAs-RNAs relationships (mirna-lnc.txt; mirna-mrna.csv), and RNAs expression data of LUAD (ENSG_ID.txt; ENSG_ID_LNC.txt; idMap.data; TCGA-LUAD.htseq_fpkm.tsv; TCGA-LUAD.mirna.tsv) used in <em>Dynamical network analysis reveals key microRNAs in progressive stages of lung cancer</em>.</p> <p> </p>
Can we assume the gene expression profile as a proxy for signaling network activity? (Supplementary Materials)
<p>These are just supplementary files.</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.
A brief exposure to rightward prismatic adaptation changes resting-state network characteristics of the ventral attentional system
<p>A brief session of rightward prismatic adaptation (R-PA) has been shown to alleviate neglect symptoms in patients with right hemispheric damage, very likely by switching hemispheric dominance of the ventral attentional network (VAN) from the right to the left and by changing task-related activity within the dorsal attentional network (DAN). We have investigated this very rapid change in functional organisation with a network approach by comparing resting-state connectivity before and after a brief exposure i) to R-PA (14 normal subjects; experimental condition) or ii) to plain glasses (12 normal subjects; control condition). A whole brain analysis (comprising all 129 regions of interest) highlighted R-PA-induced changes within a bilateral, fronto-temporal network, which consisted of 13 nodes and 11 edges; all edges involved one of 4 frontal nodes, which were part of VAN. The analysis of network characteristics within VAN and DAN revealed a R-PA-induced decrease in connectivity strength between nodes and a decrease in local efficiency within VAN but not within DAN. These results indicate that the resting-state connectivity configuration of VAN is modulated by R-PA, possibly by decreasing its modularity.</p>
How biased is our perception of plant-pollinator networks? A comparison of visit- and pollen-based representations of the same networks
<p>These are the files (.txt) used to run the analyses in the paper "<em>How biased is our perception of plant-pollinator networks? A comparison of visit- and pollen-based representations of the same networks</em>" by de Manincor et al., 2020, Acta Oecologica, volume 105 (<a href="https://doi.org/10.1016/j.actao.2020.103551">https://doi.org/10.1016/j.actao.2020.103551</a>).</p> <p>The "netwoks_txt.zip" were used to run the motifs and positions analysis and the LBMs (+ alluvial).</p> <p>The "quilt.plot_txt.zip" were used to create the quiltplots (in the Supplementary materials of the paper).</p> <p>The "plant species list" provides information about the presence of plant species in the three sites/regions, and which species have been grouped at the genus level for the comparison between visits (obs) and pollen matrix.</p> <p> </p> <p><strong>ABSTRACT</strong></p> <p>Most plant-pollinator networks are based on observations of contact between an insect and a flower in the field. Despite significant sampling efforts, some links are easier to report, while others remain unobserved. Therefore, visit-based networks represent a subsample of possible interactions in which the ignored part is variable. Pollen is a natural marker of insect visits to flowers. The identification of pollen found on insect bodies can be used as an alternative method to study plant-pollinator interactions, with a potentially lower risk of bias than the observation of visits, since it increases the number of interactions in the network. Here we compare plant-pollinator networks constructed (i) from direct observation of pollinator visits and (ii) from identification of pollen found<br> on the same insects. We focused on three calcareous grasslands in France, with different plant and pollinator species diversities. Since pollen identification always yields richer, more connected networks, we focused our comparisons on sampling bias at equal network connectance. To do so, we first compared network structures with an analysis of latent blocks and motifs. We then compared species roles between both types of networks with an analysis of specialization and species positions within motifs. Our results suggest that the sampling from observations of insect visits does not lead to the construction of a network intrinsically different from the one obtained using pollen found on insect bodies, at least when field sampling strives to be exhaustive. Most of the<br> significant differences are found at the species level, not at the network structure level, with singleton species accounting for a respectable fraction of these differences. Overall, this suggests that recording plant-pollinator interactions from pollinator visit observation does not provide a biased picture of the network structure, regardless of species richness; however, it provided less information on species roles than the pollen-based network.</p>
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>
Dataset: A methodological framework to analyse determinants of host-microbiota networks, with an application to the relationships between Daphnia magna's gut microbiota and bacterioplankton
<p>Dataset and R codes for the publication "A methodological framework to analyse determinants of host-microbiota networks, with an application to the relationships between <em>Daphnia magna</em>’s gut microbiota and bacterioplankton"</p>
Public Dataset for the Paper "Using Hybrid Bayesian Networks to Detect Audience Behaviour Changes in Youtube"
<p>This is the dataset used in the production of the paper "Using Hybrid Bayesian Networks to Detect Audience Behaviour Changes in Youtube" to be published in the EMSS 2020. Official site: http://www.msc-les.org/conf/emss2020/</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>
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.