Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
150
datasets available to search
ShareScore release 0.9.0
Dataset results
150 results for “self organization”
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>
Understanding the driver, mechanism, and role of self-organized synchronization of human crowds
<p>Raw data in "Understanding the driver, mechanism, and role of self-organized synchronization of human crowds"</p>
Data from "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps"
<p>The following files were used as data and analysis in the article "Connecting large-scale meteorological patterns to extratropical cyclones in CMIP6 climate models using self-organizing maps" (<a href="https://doi.org/10.1029/2022EF003211">https://doi.org/10.1029/2022EF003211</a>). In the study, we applied self-organizing maps (SOMs) as an automated machine-learning approach to characterize the large-scale meteorological patterns (LSMP) and associated frequency and intensity of discrete extratropical cyclone (ETC) events over the northeastern U.S. The dominant patterns of geopotential height variability are identified through SOM analysis of five reanalysis products during 1980 - 2019. ETC events are tracked using TempestExtremes and are integrated with SOMs to classify the accumulated cyclone activity associated with each pattern. We then evaluate the skill of CMIP6 historical experiments in simulating the LSMP and ETC events identified in the SOM. Please see the published paper for more details. Here we have archived: </p> <p>- data pre-processing scripts</p> <p>- code to run the self-organizing map analysis</p> <p>- code to calculate the SOM and ETC statistics</p> <p>- composites of 500-hPa geopotential height for each dataset as organized by the SOM</p> <p>- ETC tracking script and tracking output for each dataset</p> <p>- SOM output for each dataset </p>
Organized Self-Management Support Services for Chronic Depression
ClinicalTrials.gov study NCT01139060. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Redefining floristic zones in the Korean Peninsula using high-resolution georeferenced specimen data and self-organizing maps
Open the record for dataset details and reuse information.
Self-organization and information transfer in Antarctic krill swarms
Open the record for dataset details and reuse information.
Data from: Self-organizing dominance hierarchies in a wild primate population
Open the record for dataset details and reuse information.
Data from: Local interactions and self-organized spatial patterns stabilize microbial cross-feeding against cheaters
Open the record for dataset details and reuse information.
Data used for manuscript "High-integrity human intervention in ecosystems: Tracking self-organization modes"
<p>Data used for manuscript "High-integrity human intervention in ecosystems: Tracking self-organization modes".</p> <p>This includes large aerial images taken from the northern Negev in Israel, extracted areas within them, and metadata on these areas.</p>
Data from: Travelling Wave Pulse Coupled Oscillator (TWPCO) Using a Self-Organizing Scheme for Energy-efficient Wireless Sensor Networks
Recently, Pulse Coupled Oscillator (PCO)-based travelling waves have attracted substantial attention by researchers in wireless sensor network (WSN) synchronization. Because WSNs are generally artificial occurrences that mimic natural phenomena, the PCO utilizes firefly synchronization of attracting mating partners for modelling the WSN. However, given that sensor nodes are unable to receive messages while transmitting data packets (due to deafness), the PCO model may not be efficient for sensor network modelling. To overcome this limitation, the current study proposed a new scheme called the Travelling Wave Pulse Coupled Oscillator (TWPCO). For this, the study used a self-organizing scheme for energy-efficient WSNs that adopted travelling wave biologically inspired network systems based on phase locking of the PCO model to counteract deafness. From the simulation, it was found that the proposed TWPCO scheme attained a steady state after a number of cycles. It also showed superior performance compared to other mechanisms, with a reduction in the total energy consumption of 25 %. The results showed that the performance improved by 13 % in terms of data gathering. Based on the results, the proposed scheme avoids the deafness that occurs in the transmit state in WSNs and increases the data collection throughout the transmission states in WSNs.
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Supplementary material to "Manipulation of Robustness in Self-Organizing Systems on the the Example of UAV Defense"
<p>Supplementary material to the bachelor thesis "Manipulation of Robustness in Self-Organizing Systems on the the Example of UAV Defense".</p>
A precise and general FRET-based method for monitoring structural transitions in protein self-organization
<p>Data underlying the figures in the manuscript</p>
Self-Organized Patterns in Predator-Prey Droplets
Open the record for dataset details and reuse information.
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.