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180 results for “Chaos”
Dataset and software for support of the article "To Chaos or Not To Chaos: going into the resilience of the ecosystem".
<p>The CAVeg model, as well as its dependencies, and the CAVeg data files with the configurations for the two and three ecophysiological types, including the data needed to perform the Lyapunov exponents calculations with the CRAN-R DChaos package are available in this dataset.</p>
Europa Chaos Block Shapefiles and Geojson Files in Lower RegMap Images
<p>The included dataset includes the raw polygon shapefiles of the outlines for chaos blocks on Europa in the lower half of the RegMap images. The outlines were generated using the standard definitions of blocks and further subdivision in morphology of plates and knobs based on those previously published by Leonard et al. (2022) in which the author of this dataset is the same that created the majority of the Leonard et al. (2022) dataset. Images used to generate the outlines were the RegMap images within the Photogrammetrically Controlled Galileo Image Mosaics of Europa, produced by the USGS Astrogeology (Bland et al., 2021). The shapefiles do not include the entire metadata and will be uploaded at a later date, but information about chaos block morphology, area in sq km, lon/lat location of center, and chaos terrain location are included. Also included within this dataset are the Geojson files (produced by Marina Dunn) that complement the ArcMap shapefiles, so they could be implemented into other programs more easily. Both datasets have yet to be peer reviewed. </p><p> </p><p> </p><p><strong>References</strong></p><p>Leonard, E.J., Howel, S.M., Mills, A., Senske, D.A., Patthoff, D.A., Hay, H.C.F.C., and Pappalardo, R.T. (2022). Finding Order in Chaos: Quantitive Predictors of Chaos Terrain Morphology on Europa, Volume 49, Issue 8, doi: <a href="https://doi.org/10.1029/2021GL097309">10.1029/2021GL097309.</a></p><p>Bland, Michael T., Weller, Lynn A., Archinal, Brent A., Smith, Ethan, Wheeler, Benjamin H. (2021). Improving the Usability of Galileo and Voyager Images of Jupiter's Moon Europa, Earth and Space Science, Volume 8, Issue 12, doi: <a href="https://doi.org/10.1029/2021EA001935">10.1029/2021EA001935</a>.</p>
Dataset for "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos"
<p>Raw datasets used in the paper "Separate measurement- and feedback-driven entanglement transitions in the stochastic control of chaos". Use with the GitHub repository to reproduce results and figures from the referenced paper (https://github.com/clema12/CliffordBernoulli)</p>
Chaos and irreversibility of a flexible filament in periodically--driven Stokes flow
<pre>This directory contains a jupyter notebook file, generate_plots.ipynb which generate all the plots for paper "Chaos and irreversibility of a flexible filament in periodically driven Stokes flow" (arXiv:2111.14638) by Vipin Agrawal and Dhrubaditya Mitra. Readme.md file contains the information on how to use the data. If anything turns out to be incomplete, please reach to vipinagrawal25@gmail.com.</pre>
Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures
<p>Dataset used by the paper MADE: Learning to Detect and Explain Chaos in Microservice Architectures</p> <p>Include the raw dataset of 10 chaos</p>
gmap - qgis training material: Aram Chaos (Mars)
<p>Aram Chaos is a more than 250 km large crater characterized by the presence of Chaotic Terrains forming mesas and knobs, associated with the outflow channel of Ares Vallis. The Chaotic Terrains are unconformably embayed and locally superposed by some layered hydrate minerals-bearing deposits. </p> <p> </p> <p>The training package includes a complete HRSC coverage (including images and DEMs) (Neukum et al., 2004; Jaumann et al., 2007) and selected CTX (Malin et al., 2007), HiRISE (McEwen et al., 2007), and CRISM (Murchie et al., 2007) data. The area has been divided in 8 tiles each of one ‘stand.alone’ in terms of geology but at the same time ready for collaborative mapping purposes.</p>
Figure 2 -UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In order to assign some basic features of di®erent speci¯c qualitative anal-<br> ysis, let us present in what follows some basic cases of the classic analysis of<br> the 3D mixing model associated to the vortex technology presented above [4].<br> Since in the classic analysis the target is the study of the e±ciencies e¸ and<br> e´ at successive moments, the analysis is a discrete one, which aims testing<br> the special events that could appear at various, random, values of the versors<br> Mi; Nj . Therefore, the factor (D : D)1=2 which in concrete cases has numerical<br> values, has not an important signi¯cance in the numeric analysis.</p>
Figure 1-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>Starting from the importance of implementation of some optimized tech-<br> nologies for processing the polluted °uids, the bene¯ts of this technology are<br> both of scienti¯c and technologic type [6]. It concerns, one one-hand, ¯nd-<br> ing new physic- mathematical models for describing at optimal parameters<br> the turbulent mixing created by a vorticity structure, and from technologi-<br> cal standpoint, developing the vortex technology for handling the polluting<br> materials.</p>
Figure 9- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>Thus, for the non-periodic °ow, it must be noticed that a little perturbation<br> has a consistent in°uence on the model, going into a far from equilibrium<br> model. If there is annexed the irrationality of the length / surface versors<br> values (an appliance used since the beginning of the mixing study [1,2,3,4]), a<br> globally panel is obtained, with random distributed events. This space-time<br> context consolidates the basic statement that the turbulent mixing °ows must<br> be approached as chaotic systems. This is in fact regaining the idea of a system<br> / model high sensitive to initial conditions.</p>
Figure 6-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>At a ¯rst sight of the graphics, it is obvious that both in discrete and continuous case, the phenomena is not linear; there are relatively linear cases<br> { ¯g. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 4-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>At a ¯rst sight of the graphics, it is obvious that both in discrete and<br> 154continuous case, the phenomena is not linear; there are relatively linear cases<br> { ¯g. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 8- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Figure 7-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>the phenomena is not linear; there are relatively linear cases<br> fig. 4,6,7 { but especially non-linear cases { the other pictures;</p>
Figure 3- UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Figure 5-UNDERSTANDING THE MIXING PHENOMENA-FROM STRUCTURAL STABILITY TO CHAOS
<p>In fact it is about two types of phenomena for the same mixing model, and<br> this is extremely important: on one hand, in the phase-portrait analysis it is<br> very important to notice that when modifying the parameters the behavior is<br> going to be periodic { ¯g.3,5 { for the same time units, and on the other hand,<br> in the classical (discrete) analysis, when modifying the parameters there are<br> involved the so-called \rare events" [1,4], corresponding to the breakup of the<br> simulation { ¯g.8;</p>
Dataset for "Finding order in chaos: Quantitative predictors of chaos terrain morphology on Europa"
<p>Dataset S1 contains two shapefiles containing the chaos borders (ChaosBordersShapefiles.zip) and each block mapped (AllBlocksShapefiles.zip), and a text file that contains the total area (km<sup>2</sup>) of each mapped chaos terrain and the area (km<sup>2</sup>) of every block we mapped within each chaos terrain in the manuscript "Finding order in chaos: Quantitative predictors of chaos terrain morphology on Europa".</p>
The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."
<p>The data on the optical properties of kombucha and kombucha proteinoids, as well as chaos calculations are provided for the paper titled "Kombucha Mats as Responsive Materials."</p>
Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization
<p>Data for the Wind Energy Science paper "Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization".</p> <p>The data includes a file describing the wind direction distribution. The i<sup>th</sup> probability value corresponds to the probability of the wind coming between direction i and i+1.</p> <p>The other data files, corresponding to the wind farm layouts, provide the x,y coordinates of the wind turbines. </p>
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively. in A nonparametric approach in quantifying species richness of Lumbricidae in East Serbia, Balkan Peninsula
Figure. Observed (S obs) and estimated species richness for Chao 2, Jackknife 2, and Bootstrap, calculated for Lumbricidae in East Serbia. Vertical dashed lines represent 50%, 75%, and 100% of the sampling effort, respectively.
Fig. 1. The Chao 1 in Estimating fossil ant species richness in Eocene Baltic amber
Fig. 1. The Chao 1 (top line) and ACE (bottom line) richness estimates computed using Colwell (2013); note the slightly lower ACE.
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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.