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2,100 results for “nodes”
DATA7: A dataset that uses synthetic trajectories of vehicles and real cellular tower locations to simulate the workload of Edge nodes in the city of Pisa
<p><strong>Description</strong></p> <p>The dataset contains observations of vehicles in the range of edge nodes (cellular towers). The trajectories of vehicles are synthetically generated with <a href="https://www.eclipse.org/sumo/">SUMO</a>. The cellular tower positions have been taken from <a href="https://opencellid.org/">OpenCelliD</a>. The dataset is in the comma-separated values (CSV) format, and is around 220MB decompressed.</p> <p><br> The CSV contains the following fields:<br> * edge_id: unique identifier of the edge devices<br> * edge_lat: latitude coordinate of the edge device<br> * edge_lon: longitude coordinate of the edge device<br> * time: simulation step of the observation<br> * vehicle_id: unique identifier of the vehicle<br> * vehicle_lat: latitude coordinate of the vehicle<br> * vehicle_lon: longitude coordinate of the vehicle<br> * distance: geodesic distance in meters from the vehicle and the edge device</p>
Birkbeck node deployment plan for Crichton Royal Farm
<p>CSV file containing the planned deployment positions of nodal seismic sensors for PhD project.</p> <p>File contains the following fields:</p> <ol> <li><strong>UniqueID</strong> - a unique numerical identifier for each node</li> <li><strong>Line</strong> - seismic line number</li> <li><strong>Point</strong> - point identifier for each node along the line. Points ending in 01 are vertical component; 02 are horizontal North-South; 03 are horizontal East-West</li> <li><strong>Longitude</strong> - decimal degrees</li> <li><strong>Latitude</strong> - decimal degrees</li> <li><strong>Easting</strong> - UTM Easting, metres</li> <li><strong>Northing</strong> - UTM Northing, metres</li> <li><strong>ZoneNum</strong> - UTM Zone Number</li> <li><strong>ZoneLetter</strong> - UTM Zone Letter</li> </ol>
Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics"
<p>Dataset with the node discretisations employed for training advection models in "Multi-scale rotation-equivariant graph neural networks for unsteady Eulerian fluid dynamics" (https://doi.org/10.1063/5.0097679).</p> <p>The training code is available at https://github.com/mario-linov/graphs4cfd.</p>
FIGURE 4 Maximum Clade Credibility Tree inferred using a concatenate COI, 16S, 28S and 18S alignment using BEAST. Node bars are 95 in The role of allopatric speciation and ancient origins of Bathynellidae (Crustacea) in the Pilbara (Western Australia): two new genera from the De Grey River catchment
FIGURE 4 Maximum Clade Credibility Tree inferred using a concatenate COI, 16S, 28S and 18S alignment using BEAST. Node bars are 95% Higher Posterior Density, scale bar is in million years ago (Ma), starting from present 0. Numbers above bars = node age; numbers below bars (bold) = posterior probability of the node.
Data from: Vocal repertoire expansion in singing mice by co-opting a conserved midbrain circuit node
Open the record for dataset details and reuse information.
SKR and QBER calculations for a BB84-QKD link multiplexed with intense classical signal in a WSS-based node
<p>This set will comprise a set of theoretical calculations on the effect of the filtering parameters of the WSS-based node on the quantum layer performance. In more detail, this data set will consist of the calculated QBER and SKR for the phase coding BB84 protocol by considering bandwidth and shape parameters of channel bands for Liquid Crystal on Silicon (LCoS)-based WSS nodes. Critical system requirements such as insertion loss and center frequency drifts are also considered. The obtained ultra-high sensitivity of QKD link performance on the filtering parameters of the WSS-based node, as confirmed by the generated data, can be useful for monitoring and control purposes at classical networks exploiting cascaded ROADMs.</p>
Lifelong Learning of Graph Neural Networks for Open-World Node Classification
<p>Three temporal graph datasets for node classification under distribution shift.</p> <p>DBLP-Easy and DBLP-Hard are citation graph datasets. PharmaBio is a collaboration graph dataset.</p> <p>Vertices are scientific publications, edges are either citations (DBLP) or at-least-one-common-author relationships (PharmaBio).</p> <p>The task is to classify the vertices of the graph into the respective conference/journal venues (DBLP) or journal categories (PharmaBio). In the DBLP datasets, new classes may appear over time.</p> <p>Each dataset follows the structure:</p> <p>- adjlist.txt -- the graph structure encoded as adjacency lists: in each row, the first entry is the source vertex, the remaining entries are adjacent vertices</p> <p>- X.npy -- numpy serialized format for node features indexed by node id corresponding to adjlist.txt</p> <p>- y.npy -- numpy serialized format for node labels indexed by node id corresponding to adjlist.txt</p> <p>- t.npy -- numpy serialized format for time steps indexed by node id corresponding to adjlist.txt</p> <p>A paper describing our incremental training and evaluation framework is published in IJCNN 2021 (Pre-print on arXiv: <a href="https://arxiv.org/abs/2006.14422">https://arxiv.org/abs/2006.14422</a>).</p> <p>If you use these datasets in your research, please cite the corresponding paper:</p> <pre><code>@inproceedings{galke2021lifelong, author={Galke, Lukas and Franke, Benedikt and Zielke, Tobias and Scherp, Ansgar}, booktitle={2021 International Joint Conference on Neural Networks (IJCNN)}, title={Lifelong Learning of Graph Neural Networks for Open-World Node Classification}, year={2021}, volume={}, number={}, pages={1-8}, doi={10.1109/IJCNN52387.2021.9533412} }</code></pre> <p><br> </p>
Dataset for: Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes
<p>The dataset used to generate graphs for: Matthew Bradbury, Arshad Jhumka and Tim Watson. Trust Assessment in 32 KiB of RAM: Multi-application Trust-based Task Offloading for Resource-constrained IoT Nodes. Proceedings of the Symposium on Applied Computing, ACM, 2021, 1-10.</p> <p>Also includes instructions for experiment setup.</p> <p>Scripts from https://github.com/MBradbury/iot-trust-task-alloc are required to analyse and graph these results.</p>
Data from: Employing hypothesis testing and data from multiple genomic compartments to resolve recalcitrant backbone nodes in Goodenia s.l. (Goodeniaceae)
Goodeniaceae is a primarily Australian flowering plant family with a complex taxonomy and evolutionary history. Previous phylogenetic analyses have successfully resolved the backbone topology of the largest clade in the family, Goodenia s.l., but have failed to clarify relationships within the species-rich and enigmatic Goodenia clade C, a prerequisite for taxonomic revision of the group. We used genome skimming to retrieve sequences for chloroplast, mitochondrial, and nuclear markers for 24 taxa representing Goodenia s.l., with a particular focus on Goodenia clade C. We performed extensive hypothesis tests to explore incongruence in clade C and evaluate statistical support for clades within this group, using datasets from all three genomic compartments. The mitochondrial dataset is comparable to the chloroplast dataset in providing resolution within Goodenia clade C, though backbone support values within this clade remain low. The hypothesis tests provided an additional, complementary means of evaluating support for clades. We propose that the major subclades of Goodenia clade C (C1–C3 + Verreauxia) are the result of a rapid radiation, and each represents a distinct lineage.
Results for paper ""An adaptive, hanging-node, discontinuous isogeometric analysis method for the first-order form of the neutron transport equation with discrete ordinate (S_N) angular discretisation"
<p>This spreadsheet contains the results used to generate the plots in the paper "An adaptive, hanging-node, discontinuous isogeometric analysis method for the first-order form of the neutron transport equation with discrete ordinate (S_N) angular discretisation".</p>
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 2. - Bayesian (GTR+Γ+I and HKY+Γ models) and maximum likelihood 50% majority-rule consensus tree. Numbers in the nodes represent posterior probabilities (GTR+Γ+I and HKY+Γ, respectively), and bootstrap value for maximum likelihood and parsimony analyses, respectively. c1–Bragança, Pará; c2–Santa Maria do Pará, Pará; c3–National Forest of Amapá, Amapá; c4–Belém, Pará; i1–Solimões River, near Manaus, Amazonas; i2–Xingu River, Altamira, Pará; i3 and i4–Itacoatiara, Amazonas. MYBP–million years before present.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Figure 10. - Maximum-likelihood phylogeny of Epicephala species based on sequences of the COI, ArgK and EF1α genes. Numbers above nodes are maximum-likelihood bootstrap support values based on 1,000 replications. The Japanese Epicephala species are marked in blue. Symbols right to species names donate ovipositor morphology: inverted U-shape, rounded apically; inverted V-shape, acute apically.
Companion data for Asynchronous multi-phase task-based applications: Employing different nodes to design better distributions
<p>This is the companion data for the paper "Asynchronous multi-phase task-based applications: Employing different nodes to design better distributions", by Lucas Leandro Nesi, Arnaud Legrand, and Lucas Mello Schnorr, published at Future Generation Computer Systems, check the main companion at: https://gitlab.com/lnesi/companion-fgcs .</p>
PPARδ dataset curated and enriched using the Enalos tools and Enalos KNIME nodes for machine learning analysis (SCENARIOS project)
<p><span>A curated and enriched dataset for PPAR</span>δ<span>, suitable for in silico model development, was obtained from PubChem BioAssay under the numeric identifier AID 469785 using Enalos tools and Enalos KNIME nodes. This dataset comprises 136 compounds that induce luciferase activity, serving as an indicator of agonist activity against the human PPAR</span>δ<span> ligand-binding domain. These molecules were tested in a human embryonic kidney cell line (293T), co-transfected with a chimeric plasmid containing the yeast GAL4 DNA-binding domain (DBD). All 136 oxazole-based compounds retrieved from the dataset are accompanied by their half-maximal effective concentration (EC50) and enriched with 777 molecular descriptors extracted from their 2D structure using EnalosMold2 KNIME nodes</span></p>
Whole NODES Project (Data folder for the NODES project)
<p>This is the data folder for the whole NODES project.</p> <p>You can find separate subfolders downloads if you are only interested in parts of the analysis (Docker images, OptoSrc, HPN-DREAM).</p>
Computation Time, Searched Nodes and Path Length for Navigation Using Improved A-Star, Directional ORCA and FLC-ORCA
<p>Naivation simulation using Improved A*, Directional ORCA and Novel FLC-ORCA method. The computational times, searched nodes and path length are recorded for comparison. A simulation study for Robotic Navigation Aid (RNA)</p>
MetSys Node Attributes
<p>Cytoscape node attribute file for visualizing GO pathway maps (sif format) from online tool - metsys.idsl.site </p>
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data</strong></p> <ul> <li>custom_201216_m_moFluMemB_processedData.tar.gz : pre-processed data of FB5P-seq protocol (Attaf et al., 2020) on memory B cells sorted from single-cell suspensions of lungs with enzymatic digestion of lung tissue at 37°C, with index sorting information for a panel of antibodies identifying subsets of memory B cells.</li> <li>moFluMemB_DockerImages.tar.gz: Docker images used by the analysis</li> <li>moFluMemB_SingularityImages.tar.gz: Singularity images used by the analysis (conversion of the docker images)<br> </li> </ul> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5565863</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></p>
Data from: Accuracy of node and bud-scar counts for aging two dominant conifers in western North America
Accurately aging trees is critical for understanding tree demography and tree responses to environmental change. Given the proliferation of studies aimed at understanding the effects of climate and disturbance on forest ecosystems, it is important to understand the tradeoffs between field-based age estimates and precise dendrochronological techniques. We assessed the accuracy of age estimates from node counts in the field against precise tree-ring counts at the root-shoot boundary, in 1279 ponderosa pine and 1268 Douglas-fir seedlings sampled from across three study regions in the western U.S. We also assessed the accuracy of age estimates from bud-scar counts in the field against node counts and precise tree-ring counts in a subset of 757 seedlings from the Northern Rockies. Node counts systematically underestimated ring counts by an average of 4.1 years, with bias increasing with tree age. At annual, ±1-, ±2-, and ±5-yr precision, the accuracy of node counts was 5%, 15%, 29%, and 74% across all regions and species, respectively. Similar results were found for bud scars. Given the magnitude of the bias between field-based methods and ring counts, it is critical to select appropriate aging methods, based on the precision required to answer specific ecological questions. To improve the accuracy of field-based age estimates in these species, we provide a tool for correcting for the bias when precise dendrochronological aging is not feasible.
moFluMemB - Dataset : scRNA-seq from Lymph node, Spleen and Lung - 10x_191105_m_moFluMemB
<p><strong>Title</strong></p> <p>Viral infection engenders bona fide and bystander subsets of lung-resident memory B cells through a permissive mechanism<br><br><strong>Authors</strong><br>Claude Gregoire,1 Lionel Spinelli,1 Sergio Villazala-Merino,1 Laurine Gil,1 María Pía Holgado,1 Myriam Moussa,1 Chuang Dong,1 Ana Zarubica,2 Mathieu Fallet,1 Jean-Marc Navarro,1 Bernard Malissen,1,2 Pierre Milpied,1,* and Mauro Gaya1,*<br><br><strong>Affiliations</strong><br>1 Centre d'Immunologie de Marseille-Luminy (CIML), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>2 Centre d'Immunophénomique (CIPHE), Aix Marseille Université, INSERM, CNRS, Marseille, France<br>* Correspondence: milpied@ciml.univ-mrs.fr (P.M.), gaya@ciml.univ-mrs.fr (M.G.)<br><br><strong>Summary</strong><br>Lung-resident memory B cells (MBCs) provide localized protection against reinfection in the respiratory airways. Currently, the biology of these cells remains largely unexplored. Here, we combined influenza and SARS-CoV-2 infection with fluorescent-reporter mice to identify MBCs regardless of antigen specificity. We found that two main transcriptionally distinct subsets of MBCs colonized the lung peribronchial niche after infection. These subsets arose from different progenitors and were both class-switched, somatically mutated and intrinsically biased in their differentiation fate towards plasma cells. Combined analysis of antigen-specificity and B cell receptor repertoire segregated these subsets into “bona fide” virus-specific MBCs and “bystander” MBCs with no apparent specificity for eliciting viruses and generated through an alternative permissive mechanism. Thus, diverse transcriptional programs in MBCs are not linked to specific effector fates but rather to divergent strategies of the immune system to simultaneously provide rapid protection from reinfection while diversifying the initial B cell repertoire.</p> <p><strong>Data:</strong> 10x_191105_m_moFluMemB_processedData.tar.gz : pre-processed data of 10x 5’ scRNA-Seq on memory B cells sorted from single-cell suspensions of spleen, lymph nodes and lungs with mechanical dissociation of lung tissue at 4°C.</p> <p>See the three other Zenodo deposit for the rest of the data:</p> <p><strong>10.5281/zenodo.5566674</strong></p> <p><strong>10.5281/zenodo.5564624</strong></p> <p><strong>10.5281/zenodo.10559312</strong></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.