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9,152 results for “Networking”
Verdú et al.'s datasets on plant recruitment networks
<p>Plant recruitment interactions (i.e., who recruits under whom) shape the composition, diversity and structure of plant communities. Despite the huge body of knowledge on the mechanisms underlying recruitment interactions between species, we still know little about the structure of the recruitment networks emerging in ecological communities. Modeling and analyzing the community-level structure of plant recruitment interactions as a complex network can provide relevant information on ecological and evolutionary processes acting both at the species and ecosystem levels. We report a data set containing 143 plant recruitment networks in 23 countries across five continents, including temperate and tropical ecosystems. Each network identifies the species under which another species recruits. All networks report the number of recruits (i.e., individuals) per species. The data set includes >850,000 recruiting individuals involved in 118,411 paired interactions between 3,318 vascular plant species across the globe. The cover of canopy species and open ground is also provided as a separated file. Three sampling protocols were used: 1) The Recruitment Network (RN) protocol (106 networks) focuses on interactions between established plants (“canopy species”) and plants in their early stages of recruitment (”recruit species”). A series of plots are delimited within a locality and all the individuals recruiting and their canopy species are identified; 2) The paired Canopy-Open (pCO) protocol (26 networks) consists in locating a potential canopy plant and identifying recruiting individuals under the canopy and in a nearby open space of the same area; 3) The Georeferenced plot (GP) protocol (11 networks) consists in using information from georeferenced individual plants in large plots to infer canopy-recruit interactions. Some networks incorporate data for both herbs and woody species, while others focus exclusively on woody species. The location of each study site, geographical coordinates, country, locality, responsible author, sampling dates, sampling method and life habit of both canopy and recruit species are provided. This database will allow researchers to test ecological, biogeographical and evolutionary hypotheses related to plant recruitment interactions.</p> <p> </p>
Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19
<p>The biological determinants underlying the range of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multivariate model classifying COVID-19 severity (multi-class AUC<sub>training</sub> = 0.799, p-value = 4.2e-6; multi-class AUC<sub>validation</sub> = 0.773, p-value = 7.7e-6). Examination of informative model features reveals novel biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.</p>
Evaluation of Phosphoproteomics data-driven signalling network inference
<p><strong>Data</strong></p> <p>Each processed data set and its adjacency matrices representing gold standard networks are stored in one specific folder. The folders have the data set name.</p> <p>The process of obtaining the pairwise results and the evaluation metrics are divided into two main parts:</p> <p><strong>1. Pairwise results</strong></p> <ul> <li>First, all the data set folders have to be placed in one main folder.</li> <li>Then run the script: <em>RunAll_Matrices.R</em></li> <li>This script: <ul> <li>Goes through each data set folder: <ul> <li>reads the data set</li> <li>calculates for each method the resulting matrix</li> <li>Save in the data folder one file with all resulting matrices: file type RData</li> </ul> </li> </ul> </li> </ul> <p> </p> <p><strong>2. Evaluation </strong></p> <ul> <li>After this first step, each data set folder has a file that contains all the resulting adjacency matrices.</li> <li>The second step is to run the script RunAll_Golds.R</li> <li>This script <ul> <li>Goes through each folder: <ul> <li>reads the data set</li> <li>reads the result adjacency matrices from RData files</li> <li>reads the gold networks</li> <li>Calculates all evaluation metrics for each result and each gold network type.</li> </ul> </li> </ul> </li> </ul>
Small-world complex network generation on a digital quantum processor
<p>Quantum cellular automata (QCA) evolve qubits in a quantum circuit depending only on the states of their neighborhoods and model how rich physical complexity can emerge from a simple set of underlying dynamical rules. The inability of classical computers to simulate large quantum systems hinders the elucidation of quantum cellular automata, but quantum computers offer an ideal simulation platform. Here, we experimentally realize QCA on a digital quantum processor, simulating a one-dimensional Goldilocks rule on chains of up to 23 superconducting qubits. We calculate calibrated and error-mitigated population dynamics and complex network measures, which indicate the formation of small-world mutual information networks. These networks decohere at fixed circuit depth independent of system size, the largest of which corresponding to 1,056 two-qubit gates. Such computations may enable the employment of QCA in applications like the simulation of strongly-correlated matter or beyond-classical computational demonstrations.</p>
Microbiome network connectivity and composition linked to disease resistance in strawberry plants
<p>The two R data files correspond phyloseq objects used in the study. The file phyloseq_nochim_silva.RData and phyloseq_nochim_unite.RData correspond to bacterial and fungal phyloseq objects, respectively. Both objects include count table, taxonomy table and metadata information. </p>
Conservation networks do not match ecological requirements of amphibians
<p>Supplementary files of the paper Matutini et al. 2023 (https://doi.org/10.1101/2022.07.18.500425)</p> <p>Dataset sample and R script</p>
TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images
<p>This is the official repository for the paper TCRIP-MIM: Rapid Intensification Prediction for Tropical Cyclone by Combining Memory In Memory Network with Sequential Satellite Images. We use the publicly available dataset from Taiwan University (Bai et al., 2019) as experimental data, consisting of four channels of TC satellite images with a temporal resolution of 3 hours, whose preprocessing method is also publicly available. We use infrared and passive microwave TC satellite image sequences for our experiments, each divided into 24-hour segments (8 infrared and 8 passive microwave satellite images, 16 in total), preprocessing and enhancing data as noted above, so there is no experimental error due to different data preprocessing methods. In this study, the 2003–2017 TC dataset from various global basins was divided into training (1097 TCs, 43528 events), validation (188 TCs, 7884 events), and test sets (94 TCs, 3196 events).</p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Graph Neural Network for Metal Organic Framework Potential Energy Approximation
<p>Data set consists of 50,000 different configurations for the Metal Organic Framework (MOF) FIGXAU. Was generated by randomly modifying the positions of the atoms and doing an SCF relaxation on each configuration.</p>
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
<p>Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".</p>
Sars-escape network for escape prediction of SARS-COV-2
<p>This dataset belongs to a paper Sars-escape network for escape prediction of SARS-COV-2</p> <p><a>Prem Singh Bist</a>, <a>Hilal Tayara</a>, <a>Kil To Chong</a></p> <p><em>Briefings in Bioinformatics</em>, Volume 24, Issue 3, May 2023, bbad140, <a href="https://doi.org/10.1093/bib/bbad140">https://doi.org/10.1093/bib/bbad140</a></p> <p>Abstract</p> <p><strong>Motivation: </strong>Viruses have coevolved with their hosts for over millions of years and learned to escape the host's immune system. Although not all genetic changes in viruses are deleterious, some significant mutations lead to the escape of neutralizing antibodies and weaken the immune system, which increases infectivity and transmissibility, thereby impeding the development of antiviral drugs or vaccines. Accurate and reliable identification of viral escape mutational sequences could be a good indicator for therapeutic design. We developed a computational model that recognizes significant mutational sequences based on escape feature identification using natural language processing along with prior knowledge of experimentally validated escape mutants.</p> <p><strong>Results: </strong>Our machine learning-based computational approach can recognize the significant spike protein sequences of severe acute respiratory syndrome coronavirus 2 using sequence data alone. This modelling approach can be applied to other viruses, such as influenza, monkeypox and HIV using knowledge of escape mutants and relevant protein sequence datasets.</p> <p><strong>Availability: </strong>Complete source code and pre-trained models for escape prediction of severe acute respiratory syndrome coronavirus 2 protein sequences are available on Github at https://github.com/PremSinghBist/Sars-CoV-2-Escape-Model.git. </p> <p><strong>Contact: </strong>premsing212@jbnu.ac.kr.</p> <p><strong>Keywords: </strong>SARS-CoV-2; mutation; sequence analysis; viral escape prediction.</p>
Available Wireless Sensor Network and Internet of Things testbed facilities: dataset
<p>In this data set, we present data collected for the purpose of carrying out a systematic review of the available Wireless Sensor Network and Internet of Things testbed facilities. The data was collected through multiple stages and in each stage the pre-defined criteria were applied. We provide a dataset describing the hardware and software aspects of Wireless Sensor Network and Internet of Things testbed facilities available in the market and scientific community. The data were gathered through an extensive systematic review process of scientific articles published between the years 2011 and 2021. The review aims to obtain good quality data for people who are actively researching the Internet of Things facilities or anyone who is interested in that field.</p>
Figure 6 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 6. Motion of colonies toward their relevant imperialist (AtashpazGargari 2009).
Figure 4 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 4. Flowchart of Imperialist Competitive Algorithm (AtashpazGargari 2009).
Figure 7 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 7. Distribution of T. urticae in different stages of sampling.
Figure 3 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 3. Comparison of the number of interactions between frugivorous birds and plants between native forest and eucalyptus plantation in the PEIT. (a): Fecal samples interactions (P-value = 0.83, W = 3.5); (b): Focal observation interactions (P-value = 0.99, W = 4.0).
Figure 2 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 2. Interaction networks between frugivorous birds and zoochoric plants, according to the focal observations of birds in both sampled habitats. The circles represent the plant species, and the species of birds are represented by triangles. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with between species from other clusters due to its modularity (Q). (a): Fragments of native forest; (b): Fragments of eucalyptus plantation.
Figure 1 in Bird-plant interaction networks in native forests and eucalyptus plantations within a protected area
Figure 1. Interaction networks between frugivorous birds and zoochoric plants, according to the fecal samples of birds in the understory of the two sampled habitats. The circles represent the plant species, and the triangles are representing the species of birds. The acronyms in the center of the figures are the scientific names of the species (Supplementary material 1). The thickness of the links (lines) is related to the connectivity between each species (the thicker the line, the more records this interaction had). Each color represents a cluster of species that are more connected within each other than with species from other clusters due to its modularity (Q). (a): F fragments of native forest; (b): Fragments of eucalyptus plantation.
Figure 5 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 5. Complex ecological network constructed from information about interactions between frugivorous birds (triangles) and non-native plants (circles) in the Brazilian Atlantic Forest (A) and Cerrado (B). Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Names in triangles and circles represent are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
Figure 3 in Knowledge gaps regarding frugivorous ecological networks between birds and plants in Brazil
Figure 3. Complex ecological network constructed with information about interactions between frugivorous birds (triangles) and plants (circles) in the Brazilian Atlantic Forest. Each color represents an agglomerate of species that are more connected among themselves than with species of other agglomerates. Species with the highest values of connectivity and centrality are circled. Names in triangles and circles are abbreviations of species names. Abbreviations are defined in the supplementary material (S1).
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.