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2,326 results for “clusters”
Synergistic phase separation of two pathways promotes integrin clustering and nascent adhesion formation
<p><span><span><span><span><span><span><span><span><span><span><span>Integrin adhesion complexes (IACs) are integrin-based plasma membrane-associated compartments where cells sense environmental cues. The physical mechanisms and molecular interactions that mediate initial IAC formation are unclear. We found that both p130Cas ("Cas") and Focal adhesion kinase ("FAK") undergo liquid-liquid phase separation <i>in vitro</i>under physiologic conditions. Cas- and FAK- driven phase separation is sufficient to reconstitute kindlin-dependent integrin clustering <i>in vitro </i>with recombinant mammalian proteins. <i>In vitro</i> condensates and IACs in mouse embryonic fibroblasts (MEFs) exhibit similar sensitivities to environmental perturbations including changes in temperature and pH. Furthermore, mutations that inhibit or enhance phase separation <i>in vitro</i> reduce or increase the number of IACs in MEFs, respectively. Finally, we find that the Cas and FAK pathways act synergistically to promote phase separation, integrin clustering, IAC formation and partitioning of key components <i>in vitro</i> and in cells. We propose that Cas- and FAK- driven phase separation provides an intracellular trigger for integrin clustering and nascent IAC formation.</span></span></span></span></span></span></span></span></span></span></span></p>
Characteristics of individuals with moderate to severe asthma who better respond to aerobic training: a cluster analysis
<p>Database used in the study's statistical analysis</p>
Scalable clustering with supervised linkage methods
<p>This data contains protein expression values from over 25 million cells from both Lupus patients and Healthy donors (HD). The indices correspond to single cells, and the columns include the proteins, the clinical status of each cell (0 if from HD, 1 if from a lupus patient), and the cell population (membership assigned by the HAL-x clustering algorithm). </p>
Dataset - Clustering Semantic Predicates in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing predicates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>The paper instances in the dataset are grouped by ORKG comparisons and therefore the <strong><em>data.json</em></strong> file is more comprehensive than <strong><em>training_set.json</em></strong> and <strong><em>test_set.json.</em></strong></p> <p> </p> <p><strong><em>data.json</em></strong></p> <p>The main JSON object consists of a list of comparisons. Each comparisons object has an ID, label, list of papers and list of predicates, whereas each paper object has ID, label, DOI, research field, research problems and abstract. Each predicate object has an ID and a label. See an example instance below.</p> <pre><code class="language-json">{ "comparisons": [ { "id": "R108331", "label": "Analysis of approaches based on required elements in way of modeling", "papers": [ { "id": "R108312", "label": "Rapid knowledge work visualization for organizations", "doi": "10.1108/13673270710762747", "research_field": { "id": "R134", "label": "Computer and Systems Architecture" }, "research_problems": [ { "id": "R108294", "label": "Enterprise engineering" } ], "abstract": "Purpose \u2013 The purpose of this contribution is to motivate a new, rapid approach to modeling knowledge work in organizational settings and to introduce a software tool that demonstrates the viability of the envisioned concept.Design/methodology/approach \u2013 Based on existing modeling structures, the KnowFlow toolset that aids knowledge analysts in rapidly conducting interviews and in conducting multi\u2010perspective analysis of organizational knowledge work is introduced.Findings \u2013 This article demonstrates how rapid knowledge work visualization can be conducted largely without human modelers by developing an interview structure that allows for self\u2010service interviews. Two application scenarios illustrate the pressing need for and the potentials of rapid knowledge work visualizations in organizational settings.Research limitations/implications \u2013 The efforts necessary for traditional modeling approaches in the area of knowledge management are often prohibitive. This contribution argues that future research needs ..." }, .... ], "predicates": [ { "id": "P37126", "label": "activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation" }, { "id": "P36081", "label": "approach name" }, .... ] }, .... ] }</code></pre> <p> </p> <p><strong><em>training_set.json </em></strong>and<strong><em> test_set.json</em></strong></p> <p>The main JSON object consists of a list of training/test instances. Each instance has an instance_id with the format (comparison_id X paper_id) and a text. The text is a concatenation of the paper's label (title) and abstract. See an example instance below.</p> <p>Note that test instances are not duplicated and do not occur in the training set. Training instances are also not duplicated, BUT training papers can be duplicated in a concatenation with different comparisons.</p> <pre><code class="language-json">{ "instances": [ { "instance_id": "R108331xR108301", "comparison_id": "R108331", "paper_id": "R108301", "text": "A notation for Knowledge-Intensive Processes Business process modeling has become essential for managing organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes (KIPs). A KIP comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge. The previously developed Knowledge Intensive Process Ontology (KIPO) structures all the concepts (and relationships among them) to make a KIP explicit. Nevertheless, KIPO does not include a graphical notation, which is crucial for KIP stakeholders to reach a common understanding about it. This paper proposes the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models." }, ... ] }</code></pre> <p> </p> <p><strong>Dataset Statistics:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Predicates</th> <th scope="col">Research Fields</th> <th scope="col">Research Problems</th> </tr> </thead> <tbody> <tr> <td>Min/Comparison</td> <td>2</td> <td>2</td> <td>1</td> <td>0</td> </tr> <tr> <td>Max/Comparison</td> <td>202</td> <td>112</td> <td>5</td> <td>23</td> </tr> <tr> <td>Avg./Comparison</td> <td>21,54</td> <td>12,79</td> <td>1,20</td> <td>1,09</td> </tr> <tr> <td>Total</td> <td>4060</td> <td>1816</td> <td>46</td> <td>178</td> </tr> </tbody> </table> <p><strong>Dataset Splits:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Comparisons</th> </tr> </thead> <tbody> <tr> <td>Training Set</td> <td>2857</td> <td>214</td> </tr> <tr> <td>Test Set</td> <td>1203</td> <td>180</td> </tr> </tbody> </table> <p> </p>
Crater cluster data
<p>Crater cluster data and ArcMap files for crater clusters that formed after InSight landed on Mars.</p>
Young cluster simulation dynamics
<p>Velocity vectors of stars from a simulation with 200 stars: red arrows show unbound stars, blue arrows show bound stars. Gas expulsion happens at 2 Myr. The black dot shows the centre of the cluster.</p> <p>Backtracing: stars from the same simulation are backtracked using velocity vectors at t=10 Myr and assuming a linear relation. Bound stars are portrayed in blue and unbound stars in red.</p>
Original data for publication: Molecule-like and Lattice Vibrations in Metal Clusters
<p>Original Data for publication Molecule-like and Lattice Vibrations in Metal Clusters</p> <p> </p>
Figure 2 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 2 - The user selected two sub-bundles that contain streamlines representing a tractography model of the Uncinate Fasciculus.
Figure 1 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 1 - The connectivity of an ROI placed on the coronal plane over the external/extreme capsules at the level of the anterior commissure is shown (tractography method: Caverzasi et al. 2015). Each color is a cluster, as generated by the Quickbundles algorithm (Garyfallidis et al. 2012).
Figure 3 from: Jordan K, Keshavan A, Mandelli M, Henry R (2017) Cluster-viz: A Tractography QC Tool. Research Ideas and Outcomes 3: e12394. https://doi.org/10.3897/rio.3.e12394
Figure 3 - Sub-bundles that the user judged were part of an Uncinate Fasciculus tractography model are re-clustered so that the user can further refine the model.
Figure 2 from: Maumet C, Nichols T (2017) Generating and reporting peak and cluster tables for voxel-wise inference in FSL. Research Ideas and Outcomes 3: e12368. https://doi.org/10.3897/rio.3.e12368
Figure 2 - Cluster and peak tables for p<0.05 FWE-corrected voxel-wise results (dataset "fsl_thr_voxelfwep05")
Figure 1 from: Maumet C, Nichols T (2017) Generating and reporting peak and cluster tables for voxel-wise inference in FSL. Research Ideas and Outcomes 3: e12368. https://doi.org/10.3897/rio.3.e12368
Figure 1 - Examples of calls of the updated "cluster" command for voxel-wise and cluster-wise thresholds.
Data for "Coupled cluster theory for nonadiabatic dynamics: nuclear gradients and nonadiabatic couplings in similarity constrained coupled cluster theory"
Open the record for dataset details and reuse information.
Dataset for "A Clustering Analysis of Lebanese Adaptive Driving Behaviors in Response to Road Complexity" By Kobeissy et al. Submitted to The Open Transportation Journal
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Data for "Analytical evaluation of ground state gradients in quantum electrodynamics coupled cluster theory"
<p>Optimized geometries of selection of molecules in the paper.</p>
Ocean thermohaline clusters
<p>This repository has the data sets containing temperature and salinity centroids, in addition to the location of the thermohaline, temperature and salinity clusters described in a scientific publication. The data set has the position of centroids of 2 to 50 clusters of Conservative Temperature, Absolute Salinity and Thermohaline, which allows delimiting coherent thermohaline structures in the global ocean at different spatial scales. The repository also contains demo notebooks of the use of these clusters and the procedures described in the manuscript.</p> <p>If you use this repository, please include a reference to the following:</p> <p><em>Romero, E., Portela, E., Tenorio-Fernandez, L., and Sánchez-Velasco, L.: Detection of coherent thermohaline structures over the global ocean using clustering. Deep Sea Research Part I: Oceanographic Research Papers, 209:104344, ISSN 09670637. doi: 10.1016/j.dsr.2024.104344. 2024.</em></p>
IMPROVING THE METHOD OF EVALUATING THE EXPORT CAPACITY OF COTTON-TEXTILE CLUSTERS
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Fig. 2 in Morphometric Analysis And Interrelationship Of Seven Indonesian Hornbill Species (Aves, Bucerotidae) Utilizing Principal Component And Cluster Analysis
Fig. 2. Discriminant function graph of seven hornbill species based on the enter independents together model: A — genus Rhyticeros; B — genus Buceros; C — genus Anthracoceros.
Figure 7 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure 7. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/ km2 in log scale are mapped, S522c1 (A), S522c2 (B).
Figure S3 in Spatiotemporal patterns in marine fish and cephalopods communities across scales: using an autoregressive spatiotemporal clustering model. A study of fish and cephalopods of the Eastern English Channel
Figure S3. – Eastern English Channel spatial community from low (blue) to high (red) median densities of numbers/km2 in log scale are mapped, S782c1 (A), S782c2 (B), S1043sc1 (C), S1043sc2 (D), S1043sc3 (E).
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.