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2,100 results for “nodes”
Above-ground biomass and NDVI for Sensor Node Array, 2017 - ongoing.
Spatial and temporal variability characterizes virtually all ecosystems, with resource supply changing over the course of growing season and across years due to climate variation. To better understand spatial heterogeneity in ecological response across landscape positions, we established a 16-node sensor array within a 45-hectare catchment landscape that measures temporal variability of important biogeochemical and hydrological controls on ecosystem processes. The array was established at the Niwot Saddle catchment in order to accompany long term water quality and discharge records taken at the top and bottom of this catchment. The region forms an important ecological linkage between the terrestrial areas of the Niwot Ridge LTER and the aquatic component in the Green Lakes Valley. Over two years (2017 – 2018), above-ground biomass data were sampled adjacent to each of the 16 sensor nodes to characterize plant productivity. Beginning in 2023, Normalized Difference Vegetation Index (NDVI) was measured at each node.
Antigen-specific CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph node and blood following vaccination in humans
<p><strong>Abstract: </strong><br>SARS-CoV-2 infection and mRNA vaccination induce robust CD4+ T cell responses that are critical for the development of protective immunity. Here, we evaluated spike-specific CD4+ T cells in the blood and draining lymph node (dLN) of human subjects following BNT162b2 mRNA vaccination using single-cell transcriptomics. We analyze multiple spike-specific CD4+ T cell clonotypes, including novel clonotypes we define here using Trex, a new deep learning-based reverse epitope mapping method integrating single-cell T cell receptor (TCR) sequencing and transcriptomics to predict antigen-specificity. Human dLN spike-specific T follicular helper cells (TFH) exhibited distinct phenotypes, including germinal center (GC)-TFH and IL-10+ TFH, that varied over time during the GC response. Paired TCR clonotype analysis revealed tissue-specific segregation of circulating and dLN clonotypes, despite numerous spike-specific clonotypes in each compartment. Analysis of a separate SARS-CoV-2 infection cohort revealed circulating spike-specific CD4+ T cell profiles distinct from those found following BNT162b2 vaccination. Our findings provide an atlas of human antigen-specific CD4+ T cell transcriptional phenotypes in the dLN and blood following vaccination or infection.</p> <p><strong>More Information:</strong></p> <ul> <li><strong>Preprint:</strong> <a href="https://www.researchsquare.com/article/rs-3304466/v1">Research Square.</a></li> <li><strong>Sample information</strong>: data_inventory.csv file.</li> <li><strong>Code</strong> code_github_repo.zip or at the <a href="https://github.com/ncborcherding/COVID_TCR">original github repo</a></li> <li><strong>Interactive Portal</strong>: <a href="https://cellpilot.emed.wustl.edu/">CellPilot</a></li> </ul>
Dataset for a machine learning tool to improve lymph node staging with FDG-PET/CT
<p>This upload provides Open Data associated with the publication "A machine learning tool to improve prediction of mediastinal lymph node metastases in non-small cell lung cancer using routinely obtainable [<sup>18</sup>F]FDG-PET/CT parameters" by Rogasch JMM <em>et al.</em> (2022).</p> <p>The upload contains the anonymized dataset with 10 features necessary for the final GBM model that was presented in the publication. However, the original full dataset with 40 features was excluded from this Open Data repository because it may not comply with strict rules of data anonymization. The full dataset can be obtained from the corresponding author (julian.rogasch@charite.de) upon reasonable request.</p> <p>Besides the dataset, this upload provides the original python and R scripts that were used as well as their output.</p> <p>A description of all files can be found in "content_description_2022_11_19.txt".</p> <p>A user-friendly web tool that implements the final machine learning model can be found here: <a href="https://baumgagl.github.io/PET_LN_calculator/">PET_LN_calculator</a> </p>
Fedora and Debian software package dependency networks along with description text associated with nodes
<p>Fedora (version 28) and Debian (version 9.5) software package dependency networks along with description text associated with nodes. Also includes learned vectors by using PCTADW-* as in "Kexuan Sun, Shudan Zhong, and Hong Xu. 2020. Learning Embeddings of Directed Networks with Text-Associated Nodes---with Application in Software Package Dependency Networks. 2020 BigGraphs Workshop at IEEE BigData 2020."</p>
PM_152455_B_Sint_Joost_ten_Node
<u>File Name</u>: PM_152455_B_Sint_Joost_ten_Node.jpg <br><u>Sublocation</u>: Charliermuseum <br><u>Location</u>: Sint-Joost-ten-Node <br><u>Province</u>: Brussels Region <br><u>Country</u>: Belgium <br><u>Header</u>: Schilderij, James Ensor, Bloemen en vlinders; 1889, olieverf op doek, 60x50cm <br><u>Description</u>: Painting James Ensor Flowers and Butterllies 1889 Oilm on canvas 60x50cm <br><u>Keywords</u>: Belgium, Brussels region, Cultural heritage, Europe, Museum/private collection, Painting, Techniques <br><br><u>Author</u>: James Ensor (1860-1949) <br><u>Copyright</u>: Paul M.R. Maeyaert <br>
Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial
<p>Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA.<br> The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling.</p> <p>This dataset was created for a Body Part Regression tutorial.</p>
Spectral decompositions dataset for the paper "Random walk informed heterogeneities detection reveals how the lymph node conduits network influences T-cells collective exploration behavior"
<p>This file contains the left and right approximated eigenvectors, as well as the approximated eigenvalues of the networks analyzed in the paper : Random walk informed heterogeneities detection reveals how<br> the lymph node conduits network influences T-cells collective<br> exploration behavior</p>
Above and below ground phenology for the Niwot Ridge sensor node array, 2021 - 2022.
The below-ground growing season often extends beyond the above-ground growing season in tundra ecosystems. However, we do not yet know where and when this occurs and whether these phenological asynchronies are driven by variation in local vegetation communities or by spatial variation in microclimate. We deployed root in-growth cores in 4 locations in Niwot Ridge’s sensor node array as part of a multi-site study of above- and below-ground tundra phenology.
Undirected Node Attributed Social Network Graph of Twitter Users interested in plastic pollution - created in the framework of the PlasticTwist project
<p>This dataset has been created in the framework of the Plastic Twist project (<a href="https://ptwist.eu/">Ptwist</a>) and more specifically using the Ptwist crowdsourcing application (<a href="https://crowdsourcing.plastictwist.com/">crowdsourcing.plastictwist.com/</a>). We are sharing the edge list and specific node attributes (hashtags) of Twitter users posting about plastic pollution. The dataset can be used for community detection,clustering, node importance, influence maximization tasks, etc. Each user is represented by a unique integer which has nothing to do with the official Twitter user ID. The dataset contains three (3) files: </p> <ul> <li>ptwist.edgelist: A list containing all the 1,362,863 edges between the users. When loaded they create an undirected graph of 800K+ users.</li> <li>node_attributes.txt: This file contains information about the hashtags used by each user. (e.g. "652003": ["SingleUsePlastic"] -> user 6529003 has used the hashtag SingleUsePlastic) </li> <li>annotated_graph: A pickle file which, when loaded, returns a <a href="https://networkx.github.io/">NetworkX</a> node attributed undirected graph.</li> </ul> <p> </p> <p> </p>
Taming the fixed-node error in diffusion Monte Carlo via range separation
<p>Suplementary information.</p> <p>Contains the org-mode computational notebook with all the input data (geometries, basis sets, pseudo-potentials) and output data (computed energies, densities, number of determinants) related to the article.</p> <p>A csv file is created by the notebook and an HTML export of the notebook is also provided.</p>
Relations in the Biographical Dictionary of Republican China - Node & Edge lists and Attribute File
<p>This dataset contains the node and edge lists of relations in the BDRC. They are based on the "Relations in the Biographical Dictionary of Republican China - Standardized output" file in this repository. The attributes of the nodes refer only to the main 589 figures in the BDRC.</p>
Air/Snow temperature vertical profiles at different nodes of the 'Limnopolar Lake' CALM site, in Byers Península Livingston Island, Antarctica (2013-2022)
<div> <div> </div> </div> <div> <p>Air or seasonal snow temperature data were collected at different heights above the ground between 2013 and 2022 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Limnopolar Lake' CALM site (A25) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness on Byers Peninsula, Livingston Island, South Shetland Islands, Antarctica.</p> <p>In 2013, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active for only one year and is now discontinued.</p> <p>Between 2017 and 2022, three arrays were installed at nodes (00,00), (05,05), and (10,10). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. This experiment, referred to as 'HR', has also been discontinued.</p> </div>
Air/Snow temperature vertical profiles at different nodes of the 'Crater Lake' CALM site in Deception Island, Antarctica (2012-2023)
<p>Air or seasonal snow temperature data were collected at different heights above the ground between 2012 and 2023 using an array of temperature micro-loggers (iButton models by Maxim) mounted on vertical wooden masts. These measurements were conducted at various nodes within the 100x100 m 'Crater Lake' CALM site (A16) grid of the PERMATHERMAL network, managed by the University of Alcalá, Madrid, Spain, to monitor active layer thickness in Deception Island, South Shetland Islands, Antarctica.</p> <p>In 2012, nine arrays were installed at nodes with relative coordinates (00,00), (00,05), (00,10), (05,00), (05,05), (05,10), (10,00), (10,05), and (10,10). Measurements were taken at heights of 2.5, 5, 10, 15, 20, 25, 30, and 40 cm above the ground surface using DS1921G iButton loggers, which recorded air/snow temperatures every 4 hours. This experiment, referred to as 'Mini', was active until early 2021.</p> <p>Between 2017 and 2023, four arrays were installed at nodes (00,010), (05,05), (06,00), and (10,00). These arrays measured air/snow temperatures at heights of 2.5, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 120, 140, and 160 cm above the ground surface using DS1922L iButton loggers, which recorded temperatures every 3 hours. Three of the arrays of this experiment, referred to as 'HR', has also been discontinued in early 2021, althought one of them was active until early 2024.</p>
myExperiment Workflows, "abstracted" (all non-analytical nodes removed)
<p>To do the SCOFF analysis (detecting highly similar workflow fragments) we took all bioinformatics-related workflows from myExperiment and removed all non-analytical nodes. These included nodes referred-to as "shims" - those that do data structure/type transformations, but not any "semantic" transformation. This deposit contains all such abstracted workflows.</p>
Node-layer duality in networked systems: multilayer brain networks dataset
<p>This dataset contains the processed data used in<a href="https://doi.org/10.1038/s41467-024-50176-5"> Presigny, Corsi and De Vico Fallani (2024)</a>. It is made available for replication purposes. The code to treat these data is available <a href="https://github.com/Presigny/multilayer_duality">here</a>.</p> <p>Multilayer brain networks are obtained from the experimental data published in <a>Guillon <em>et al. </em>(2017)</a>. 23 Alzheimer’s diseased (AD) patients and 27 healthy age-matched control (HC) subjects, participated in the study. For each subject, 6 minutes resting-state eyes-closed brain activity was recorded noninvasively using a whole-head MEG system with 102 magnetometers and 204 planar gradiometers (Elekta Neuromag TRIUX MEG system) at a sampling rate of 1000Hz. Signal artefacts were removed using different techniques including removed signal space separation, principal component analysis, and visual inspection. Finally, source-imaging was used to project the signals from the sensor to the source space consisting of N = 70 regions of interest (ROI) defined by the Lausanne cortical atlas parcellation (see file name_of_ROIs.txt for the order). Here, we used spectral bicoherence to estimate functional connectivity between ROIs and between frequencies of brain activity. Specifically, we considered M = 77 layers corresponding to frequencies in the 2 − 40 Hz range with a resolution of 0.5 Hz. Other parameters were non overlapping windows of 2s averaged according to the Welch method. The resulting networks are full-multilayer consisting of both intralayer and interlayer connections, including weighted links between replica nodes.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
2746-node Polish Energy System Data of Transmission and Voltage Constraints Contribution to the Formation of LMP
<p>This is the dataset that is used for the original article: "Contribution of Transmission and Voltage Constraints in the Formation of Locational Marginal Prices"</p> <p> </p> <p>MATPOWER is required. The dataset obtained by MATPOWER ver6.0 tool [1].</p> <p>Run run_matpower_acopf.m in MATLAB to start AC OPF. Offers should be saved in the MATLAB search path. Results of AC OPF are saved in bus, branch and gen files. Column names correspond to MATPOWER case file.</p> <p>Price-bonding factors are saved in lambda_P_PBF and lambda_Q_PBF. Ones in MP, MQ columns correspond to marginal nodes for real and reactive power respectively. Ones in CV, CD columns correspond to controlled voltage magnitude and phase respectively. </p> <p>Acronyms in column names:</p> <ul> <li>C(N) - bidding price of a marginal generator at node N </li> <li>Fmax(N) - maximum allowable real power throught line N, where N - index number of a line in branch.csv with binding constraint</li> <li>LAMP_P - LMP for real power</li> <li>LAM_Q - LMP for reactive power</li> <li>PBF - price-bonding factor</li> <li>TC - transmission constraint</li> <li>VC - voltage constraint</li> <li>V(N) - maximum (minimum) allowed voltages at node N</li> </ul>
User stories and xAPI statements for "A mobile campus application as a sensor node for Personal Learning Environments"
<p>This dataset provides the full user stories and xAPI statements as used in the prototype described in the article "A mobile campus application as a sensor node for Personal Learning Environments". It consists of two PDF documents described below. The files were created as part of the master thesis of Hendrik Geßner.</p> <p>"User Stories.pdf" contains a complete list of user stories with required context information, existing portlets and a category. The process that led to this collection is described very briefly in the article mentioned above, a graphical explanation is available in the attached image "Use case process complete.jpg"</p> <p>"xAPI Statements.pdf" contains all xAPI statements used in the prototype described in the article mentioned above. Dynamic elements such as names or IDs are highlighted on color. The statements appear in the following order: attended, used, loggedin, wasat, opened, closed, joined, left.</p>
EDGELESS: orchestration domain with 5 VM nodes
<div> <h2>Scenario</h2> <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#scenario"></a></div> <ul> <li>ε-ORC, ε-CON, Redis in a VM</li> <li>5 nodes in other VMs: <ul> <li>4 with 2 cores</li> <li>1 with 8 cores</li> </ul> </li> </ul> <p>Factors:</p> <ul> <li>load, in terms of the interarrival between consecutive workflows, with the average ranging from 10 s to 60 s</li> <li>orchestration policy, i.e., either <code>Random</code> or <code>RoundRobin</code></li> </ul> <p>Each experiment lasts 1 hour and is repeated 10 times.</p> <p>Workload generated with <code>edgeless_benchmark</code>, see <code>run.sh</code> for the complete set of parameters.</p> <div> <h2>Repeatability</h2> <p>Check GitHub repository with the EDGELESS <a href="https://github.com/edgeless-project/edgeless/">reference implementation</a> and <a href="https://github.com/edgeless-project/cnr-experiments">experiments</a> (experiment <code>003-tid-benchmark-varload</code>)</p> <a href="https://github.com/edgeless-project/cnr-experiments/tree/main/experiments/003-tid-benchmark-varload#repeatability"></a></div> <ol> <li>Install a Redis server</li> <li>Update the ε-ORC and ε-CON configuration in <code>conf/</code></li> <li>Install a cluster of EDGELESS nodes</li> <li>Use <code>run.sh</code> to run experiments by saving the output data in <code>dataset</code></li> </ol> <h2>Dataset content</h2> <p>The compressed archive <code>003-tid-benchmark-varload.zip</code> contains the following files, all with CSV entries:</p> <table> <tbody><tr> <th>Filename</th> <th>Format</th> </tr> </tbody><tbody> <tr> <td><code>health_status.csv</code></td> <td>timestamp,node_id,node_health_status</td> </tr> <tr> <td><code>capabilities.csv</code></td> <td>timestamp,node_id,node_capabilities</td> </tr> <tr> <td><code>mapping_to_instance_id.csv</code></td> <td>timestamp,logical_id,node_id1,physical_id1,...</td> </tr> <tr> <td><code>performance_samples.csv</code></td> <td>metric,identifier,value,timestamp</td> </tr> <tr> <td><code>application_metrics.csv</code></td> <td>entity,identifier,value,timestamp</td> </tr> </tbody> </table> <p>Notes:</p> <ul> <li>The timestamp format is always A.B, where A is the Unix epoch in seconds and B is the fractional part in nanoseconds.</li> <li>All the identifiers (node_id, logical_id, and physical_id) are UUID.</li> <li>The field entity in the application metrics can be <code>f</code> (function) or <code>w</code> (workflow).</li> </ul>
[Source code and Dataset] FrozenQubits: Boosting Fidelity of QAOA by Skipping Hotspot Nodes
<p>FrozenQubits is a novel quantum divide-and-conquer approach that leverages the insight about most real-world applications having power-law graphs to boost the fidelity of QAOA applications. In FrozenQubits, we freeze some of the nodes with the highest degree of connectivity. This drops the edges connected to these nodes resulting in a circuit with fewer CNOTs and lower depth.</p>
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.