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133 results for “Self-organization”
Data from: Self-organizing nervous systems for robot swarms
Open the record for dataset details and reuse information.
High frequency soil sensor data for SOM input - Complex drivers of riparian soil oxygen variability revealed using self-organizing maps
The provided datasets contain the original (non-normalized) high-frequency soil and meteorological observations that were fed to the Self-Organizing Map (SOM) in order to identify ranges of values associated with low and high soil O2 conditions. For the Champlain Valley (CV) site we used the natural breaks algorithm to subset the data into high and low O2 datasets. O2 values were consistently low at the Green Mountains (GM) site, so we ran a single SOM for all O2 values at this site. The original values were then range-normalized before they were fed to the SOM.
Tropical North Atlantic phases: A Self-Organizing Maps (SOM) approach.
<p>Data from SOM analysis</p>
Self-Organized Construction with Continuous Building Material: Higher Flexibility based on Braided Structures
<p>Paper material.</p>
Controlling noise with self-organized resetting
<p>Data used to generate the figures for the manuscript "Controlling noise with self-organized resetting".</p>
Simple Physical Interactions Yield Social Self-Organization in Honeybees - datasets
<p>Empirical data of the location of the bees in complex thermal environments in specific time intervals. For more details please refer to <br> <br> Szopek M, Stokanic V, Radspieler G and Schmickl T (2021) Simple Physical Interactions Yield Social Self-Organization in Honeybees. <em>Front. Phys.</em> 9:670317. doi: 10.3389/fphy.2021.670317</p> <p> </p> <p>exp_1.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 9 repetitions of Experiment 1 (static thermal environment with one global optimum at 36°C and a pessimum at 30°C).</p> <p>exp_2.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 8 repetitions of Experiment 2 (static thermal environment with one global optimum at 36°C and one local optimum at 32°C).</p> <p>exp_3.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 30 min) for each of the 6 repetitions of Experiment 3 (static thermal environment with two equal optima of 36°C).</p> <p>exp_4.csv contains the percentage of bees in the left, the center and the right evaluation zone in 1-minute intervals (runtime 105 min) for each of the 17 repetitions of Experiment 4 (dynamic thermal environment).</p> <p>exp_5.csv contains the percentage of bees in the left, the center and the right evaluation zone at minute 30 for i) each of the 10 repetitions of Experiment 5 (static thermal environment with social stimulus) and ii) for each of the 8 repetitions of an experiment with the same thermal environment but without a social stimulus for comparison.</p>
Self-organization of conducting pathways explains complex wave trajectories in procedurally interpolated fibrotic cardiac tissue: a digital-twin study
<p><span>In precision cardiology, digital twinning technology (DT) holds promise for predicting arrhythmias by </span><span>leveraging patient data and biophysics knowledge. However, current DTs are designed to directly reproduce biopotential conduction in cardiac tissue, while only indirect non-invasive methods can be clinically implemented on real organs. This discrepancy challenges our understanding of DT applicability limits. This study aims to enhance DT by developing an in-vitro training complement. We conducted a frame-by-frame comparison of in-vitro optical mapping of biopotential conduction with machine learning (ML) optimized DT predictions. Patient-specific self-organized tissue samples of human induced pluripotent stem cells-derived cardiomyocytes (CMs) with diffuse fibrosis served as DT prototypes. High spatiotemporal resolution optical mapping recordings (</span><span>Δ</span><span>x=117 ± 4 </span><span>μ</span><span>m, </span><span>Δ</span><span>t=7.69 ms) and immunostainings were used to reproduce fibrotic samples with a linear size of 7.5 mm. Using data-driven ML-optimization of the Cellular Potts model, we examined wave propagation at the subcellular level. The modified Glazier-Graner-Hogeweg model accurately reflected the “perinatal window” until the 20th day of differentiation, affecting CMs self-organization. The percolation threshold of virtual conductive pathways reached 26% (26.7 ± 2.9% of CMs in-vitro), resulting in a spatial correlation of amplitude maps between prototype samples and their DT with Pearson’s coefficients of 0.83 ± 0.02. As a proof-of-concept, we demonstrated the ability of ML-optimized DT to predict and interpolate wavefront trajectories in optical mapping recordings. We found that mathematical approximation of fibrosis distribution played a key role in DT prediction accuracy, potentially informing the implementation of LGE-MRI detection of fibrosis within cardiac DT frameworks.<br><br>Dataset A: <span>Immunostaining images were sorted based on the day of enzymatic disaggregation (before and after day 20). We collected and sorted </span><span>α</span><span>-actinin, Connexin43 and DAPI immunostainings </span><span>for Cellular Potts Model optimization. During data processing, cell shape<span>s (n=109 and n=69 for CM and BPs respectively after day 20, n=209 and n=90 for CM and BPs respectively before day 20) were formalized.<br>Dataset C corresponds to FluoVolt recordings <span>(3 samples). Dataset B corresponds to Fluo-4 AM recordings in iPSC-CMs samples with diffuse fibrosis imitation (4 samples)</span>. </span></span></span></p>
In materia reservoir computing with a fully memristive architecture based on self-organizing nanowire networks - Dataset
<p>This is the dataset of "<em>In materia</em> reservoir computing with a fully memristive architecture based on self-organizing nanowire networks"</p>
Machine-guided path sampling to discover mechanisms of molecular self-organization (Training and validation data)
<p>Training and validation data for the Nature Computational Science manuscript "Machine-guided path sampling to discover mechanisms of molecular self-organization"</p>
Reducing Uncertainty in Collective Perception using Self-organized Hierarchy
<p>This dataset accompanies an article submission and a <a href="https://github.com/BlueDiamond07/Collective_perception">code repository</a>.</p> <p><strong>Abstract:</strong><br> In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this research, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organized hierarchy as an approach to improve accuracy in collective perception, by reducing or eliminating some of the sources of uncertainty. Using self-organized hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that have been designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organized hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for time-invariant and time-varying absolute conditions, and test the scalability and fault tolerance of their accuracies. We show that the self-organized hierarchy approach is substantially more accurate, more consistent, and faster than the other approaches, but also that it is comparably scalable and fault-tolerant.</p>
Tomography of memory engrams in self-organizing nanowire connectomes - Dataset
<p>This is the dataset of "Tomography of memory engrams in self-organizing nanowire connectomes"</p>
Beyond mutations: accounting for selection and self-organization in the analysis of protein evolution
Open the record for dataset details and reuse information.
A Bibliography on Self-Organization in Manufacturing Systems
<p>Bibliographic Review on Self-Organization in Manufacturing Systems</p> <p>Content:</p> <ul> <li><code>README.md</code> (main documentation)</li> <li><code>pipeline.png</code> (processing pipeline from bibliographic sources to results and figures)</li> <li><code>script/</code> (JS files for data transformation and N3, to use with Linked-Data-Fu, for data collection)</li> <li><code>data/</code> (intermediary RDF files including bibliographic data and citation networks)</li> <li><code>query/</code> (SPARQL query files to process intermediary RDF files)</li> <li><code>result/</code> (TSV and image files as the main result of the bibliographic review)</li> </ul>
Data from: Local interactions and self-organized spatial patterns stabilize microbial cross-feeding against cheaters
Mutualisms are ubiquitous, but models predict they should be susceptible to cheating. Resolving this paradox has become relevant to synthetic ecology: cooperative cross-feeding, a nutrient exchange mutualism, has been proposed to stabilize microbial consortia. Previous attempts to understand how cross-feeders remain robust to non-producing cheaters have relied on complex behavior (e.g., cheater punishment) or group selection. Using a stochastic spatial model, we demonstrate two novel mechanisms that can allow cross-feeders to outcompete cheaters, rather than just escape from them. Both mechanisms work through the spatial segregation of the resources, which prevents individual cheaters from acquiring the resources they need to reproduce. First, if microbe dispersal is low but resources are shared widely, then the cross-feeders self-organize into stable spatial patterns. Here the cross-feeders can build up where the resource they need is abundant, and send their resource to where their partner is, separating resources at regular intervals in space. Second, if dispersal is high but resource sharing is local, then random variation in population density creates small-scale variation in resource density, separating the resources from each other by chance. These results suggest that cross-feeding may be more robust than previously expected and offer strategies to engineer stable consortia.
Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </p>
Self-organization and information transfer in Antarctic krill swarms
<p>Antarctic krill swarms are one of the largest known animal aggregations, and yet, despite being the keystone species of the Southern Ocean, little is known about how swarms are formed and maintained. Understanding the local interactions between individuals that provide the basis for these swarms is fundamental to knowing how swarms arise in nature, and what potential factors might lead to their breakdown. Here we analyzed the trajectories of captive, wild-caught krill in 3D to determine individual level interaction rules and quantify patterns of information flow. Our results demonstrate that krill align with near neighbors and that they regulate both their direction and speed relative to the positions of groupmates. These results suggest social factors are vital to the formation and maintenance of swarms. Further, krill operate a novel form of collective organization, with measures of information flow and individual movement adjustments expressed most strongly in the vertical dimension, a finding not seen in other swarming species. This research represents a vital step in understanding the fundamentally important swarming behavior of krill.</p>
Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids
<p>Source data and source code for the graphs in "Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids".</p>
Self-organized transport model of spark discharge development and its application to the process of lightning initiation in a thundercloud
<p>This video visualizes results obtained by a small-scale transport model of electrical discharge formation in a thundercloud. The model discharge tree is a dynamic graph, nodes and edges of which are capacitive and conductive elements, respectively, electrical parameters of which vary with time. In the framework of the used approach, a heated well-conducting lightning leader channel is formed by combining the currents of tens of thousands of streamers, each of which initially has a negligible conductivity and a temperature, which does not differ from the ambient value. The model leader has electrical characteristics, which are intermediate between the laboratory long spark and the developed lightning channel, which is expected for an “immature” lightning leader.</p>
Self-organizing map of electron-impact mass-spectra
<p>This text file is a self-organising map (256 x 256 grid units) of mass spectra.</p> <p>The mass spectra are stored as a continuous list. In the file, consecutive sets of 256 rows in the file are one row of the map (each row has 256 grid units). After every 256th row in the file, a new map row begins (256 rows in total).</p> <p>The mass spectra are electron-impact mass-spectra, as they are typically recorded in gas chromatography. The resolution is unit mass, the spectral range is limited to 29 - 200 m/z, and the spectral intensities are normalized.</p> <p>The mass spectra were collected from the Mass Bank of North America (<em>MassBank of North America</em>. <a href="https://mona.fiehnlab.ucdavis.edu/">https://mona.fiehnlab.ucdavis.edu/</a> (accessed 2018-07-05)) and are used under a CC-BY 4.0 license.</p>
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
<p>The use of biota to analyze the distribution pattern of biogeographic regions is essential to gain a better understanding of the ecological processes that cause biotic differentiation and biodiversity at multiple spatiotemporal scales. Recently, the collection of high-resolution biological distribution data (e.g., specimens) and advances in analytical theory have led to the quantitative analysis and more refined spatial delineation of biogeographic regions. This study was conducted to redefine floristic zones in the southern part of the Korean Peninsula and to better understand the eco-evolutionary significance of the spatial distribution patterns. Based on 309,333 distribution data of 2,954 vascular plant species in the Korean Peninsula, we derived floristic zones using self-organizing maps. We compared the characteristics of the derived regions with those of historical floristic zones and ecologically important environmental factors (climate, geology, and geography). In the clustering analysis of the floristic assemblages, four distinct regions were identified, namely, the cold floristic zone (Zone I) in high-altitude regions at the center of the Korean Peninsula, cool floristic zone (Zone II) in high-altitude regions in the south of the Korean Peninsula, warm floristic zone (Zone III) in low-altitude regions in the central and southern parts of the Korean Peninsula, and maritime warm floristic zone (Zone IV) including the volcanic islands Jejudo and Ulleungdo. Totally, 1,099 taxa were common to the four floristic zones. Zone IV showed the highest abundance of specific plants (those found in only one zone), with 404 taxa. Our study improves floristic zone definitions using high-resolution regional biological distribution data. It will help better understand and re-establish regional species diversity. In addition, our study provides key data for hotspot analysis required for the conservation of plant diversity.</p>
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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.