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309 results for “swarm”
Fracture swarm formation during shut-in driven by pore pressure waves [dataset]
<p>Open access data for the paper entitled "Fracture swarm formation during shut-in driven by pore pressure waves".</p>
Persistence of an endangered native duck, feral mallards, and multiple hybrid swarms across the main Hawaiian Islands
Interspecific hybridization is recognized as an important process in the evolutionary dynamics of both speciation and the reversal of speciation. However, our understanding of the spatial and temporal patterns of hybridization that erode versus promote species boundaries is incomplete. The endangered, endemic koloa maoli (or Hawaiian duck, Anas wyvilliana) is thought to be threatened with genetic extinction through ongoing hybridization with an introduced congener, the feral mallard (A. platyrhynchos). We investigated spatial and temporal variation in hybrid prevalence in populations throughout the main Hawaiian Islands, using genomic data to characterize population structure of koloa, quantify the extent of hybridization, and compare hybrid proportions over time. To accomplish this, we genotyped 3,308 double-digest restriction-site-associated DNA (ddRAD) loci in 425 putative koloa, mallards, and hybrids from populations across the main Hawaiian Islands. We found that despite a population decline in the last century, koloa genetic diversity is high. There were few hybrids on the island of Kauaʻi, home to the largest population of koloa. By contrast, we report that sampled populations outside of Kauaʻi can now be characterized as hybrid swarms, in that all individuals sampled were of mixed koloa × mallard ancestry. Further, there is some evidence that these swarms are stable over time. These findings demonstrate spatial variation in the extent and consequences of interspecific hybridization, and highlight how islands or island-like systems with small population sizes may be especially prone to genetic extinction when met with a congener that is not reproductively isolated.
Data from: Oceanic swarms of Antarctic krill perform satiation sinking
Antarctic krill form some of the highest concentrations of animal biomass observed in the world's ocean potentially due to their prolific ability to swarm. Determining the movement of Antarctic krill within swarms is important to identify drivers of their behaviour and their biogeochemical impact on their environment. We examined vertical velocity within approximately 2000 krill swarms through the combined use of a shipborne echosounder and an acoustic Doppler current profiler (ADCP). We revealed a pronounced downward anomaly in vertical velocity within swarms of -0.6 cm.s-1 compared with vertical motion outside the swarm. The anomaly changed over the diel cycle, with smaller downward anomalies occurring at night. Swarms in regions of high phytoplankton concentrations (a proxy for food availability) also exhibited significantly smaller downward anomalies. We propose that the anomaly is the result of downward velocities generated by the action of krill beating their swimming appendages. During the night and in high phytoplankton availability, when krill are more likely to feed to the point of satiation, swimming activity is lowered and the anomaly is reduced. Our findings are consistent with laboratory work where krill ceased swimming and adopted a parachute posture when sated. Satiation sinking behaviour can substantially increase the efficiency of carbon transport to depth through depositing faecal pellets at the bottom of swarms, avoiding the reingestion and breakup of pellets by other swarm members.
Data from: Testing the limits of pheromone stigmergy in spatially constrained robotic swarms
Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromones deposited into a shared environment to coordinate behaviour through stigmergy. Repellent pheromones can facilitate rapid dispersal of robotic agents, yet this has been demonstrated only for relatively small swarm sizes (N<30). Here, we report findings from swarms of real robots (Kilobots) an order of magnitude larger (N>300), and from realistic simulation experiments up to N=400. We identify limitations to stigmergy in a spatially constrained environment – a free but bounded two-dimensional workspace – using repellent binary pheromone. At larger N a simple, stigmergic avoidance algorithm becomes first no better, then inferior to, the area coverage of non-interacting random walkers. Thus, with ever-increasing swarm sizes, the assumption of robustness and scalability for such approaches may need to be re-examined. Instead, subcellular biology, and diffusive processes, may prove a better source of inspiration at large N in spatially constrained or high agent density environments.
Seismological dataset for 2018 West Bohemia earthquake swarm
<p>Dataset used in the study: T. Eulenfeld (2020), Toward source region tomography with inter-source interferometry: Shear wave velocity from 2018 West Bohemia swarm earthquakes, <em>Journal of Geophysical Research: Solid Earth</em>, 125, e2020JB019931, doi: <a href="https://dx.doi.org/10.1029/2020JB019931">10.1029/2020JB019931</a>.</p> <p>The dataset includes</p> <ul> <li>HYPODD pha file with relocated earthquake catalog</li> <li>HYPODD pha file with relocated earthquake catalog of selected high quality events (Eulenfeld, 2020)</li> <li>Text file with focal mechanisms of 13 largest earthquakes</li> <li>Text file with coordinates of 9 WEBNET stations</li> <li>StationXML file with coordinate and response information of 9 WEBNET stations (prepared from RESP files by T. Eulenfeld)</li> <li>MSEED files of waveforms of earthquakes</li> </ul> <p>Citation for waveforms:</p> <p>Institute of Geophysics, Academy of Sciences of the Czech Republic (1991): West Bohemia Local Seismic Network. International Federation of Digital Seismograph Networks. Dataset/Seismic Network. <a href="https://www.doi.org/10.7914/SN/WB">10.7914/SN/WB</a></p> <p>Citation for earthquake catalog:</p> <p>Bachura M, Fischer T, Doubravová J, Horálek J, From earthquake swarm to a main shock–aftershocks: the 2018 activity in West Bohemia/Vogtland (2021), <em>Geophysical Journal International</em>, 224 (33): 1835–1848, doi: <a href="https://doi.org/10.1093/gji/ggaa523">10.1093/gji/ggaa523</a></p> <p>Citation for focal mechanisms:</p> <p>Plenefisch T and Barth L (2019), The May 2018 earthquake swarm in Vogtland/NW-Bohemia: Spatiotemporal evolution and focal mechanism determinations, in Geophysical Research Abstracts, volume 21, EGU2019–9356</p> <p> </p> <p>Version 2:</p> <ul> <li>Version 1 of the data set included only waveforms for earthquakes with magnitude larger than 1.8, version 2 of the data set includes almost all waveforms for earthquakes listed in the catalog</li> <li>Added StationXML file</li> <li>Added catalog with selected high quality events</li> </ul>
Data for 3 Swarm-E Fast Auroral Imager Passes Observing the ICEBEAR Radar Field of View
<p>Files containing data for 3 Swarm-E satellite passes observing the Ionospheric Continuous-wave E-region Bistatic Experimental Auroral Radar (ICEBEAR) field of view during semi-active geomagnetic conditions using the Fast Auroral Imager. The dates and times of the passes are:</p> <p>2018-03-10 05:21:00-05:26:00 UT<br> 2019-10-27 04:03:00-04:13:00 UT<br> 2020-03-19 09:00:00-09:04:00 UT</p>
FIG. 3 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 3. Examples illustrating the recovered movement patterns between maternity colonies and swarming sites. Individuals at swarming sites LA (A) and KG (B) were recovered at multiple colonies. Likewise, individuals recovered at colonies A (C) and H (D) were caught at different swarming sites on the same night. Capture site abbreviations and maternity colony IDs correspond to those used in Fig. 1 and Table 1
FIG. 2 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 2. Map showing the minimum catchment polygon (dark grey), and maximum range circle (light grey) of the two main swarming sites (LA, KG). Country border between Belgium and the Netherlands (irregular black line), forest fragments (irregular grey patches), swarming sites (grey pentagons) and recovered roost sites (black dots) are also indicated
FIG. 1 in Swarming behaviour, catchment area and seasonal movement patterns of the Bechstein's bats: implications for conservation
FIG. 1. Map of the Belgium and adjacent countries (inset top right) indicating the location of study area. Within the study are (main figure), sampled swarming sites and recovered roost sites are indicated (grey pentagons and black circles, respectively). Forest fragments are shaded according to age (recent: light grey; ancient: dark grey). Capture site abbreviations correspond to those used in Table 1
FIG. 1 in How do young bats find suitable swarming and hibernation sites? Assessing the plausibility of the maternal guidance hypothesis using genetic maternity assignment for two European bat species
FIG. 1. Left — drawing of 'Brunnen Meyer'; right — photo of the western side of the 'Brunnen Meyer' well house with window where bats enter
FIG. 2 in How do young bats find suitable swarming and hibernation sites? Assessing the plausibility of the maternal guidance hypothesis using genetic maternity assignment for two European bat species
FIG. 2. Simulation (10,000 iterations) of how often mother-offspring pairs are expected to arrive on the same night by chance for M. daubentonii (top) and M. nattereri (bottom); the observed number of pairs arriving on the same night is denoted by the arrow
Magnetic pictures of the Gulf Stream and Kurosio as they seen by SWARM satellites
<p>Six MATLAB/Octave data files (*.mat) are the output of two codes: myfilter_Gulf_Stream.m & myfilter_Kurosio.m. Each file containes three fileds: x10, y10, and ogmf10; all three have the same dimention. The x10 is 2D longitude, the y10 is 2D latitude, the ogmf10 is the filtered ocean generated magnetic field on 1.0 degree grid. The eaziest way to see the fields in Matlab/Octave is the comand <strong>contourf(x10,y10,ogmf10,10)</strong>. The files names containing 'gulfstream' are the Gulf Stream induced magnetic fields; the files with names starting with 'kurosio' are the inferred fild for the Kurosio current. Letter "A" in the name is for Alpha satellite, letter "C" is for Charlie satellite, letters "AC" denote combined two-satellite dataset. Number "430" means altitude in km. "202204" is April 2022. </p> <p>The Matlab/Octave codes myfilter_Gulf_Stream.m & myfilter_Kurosio.m are filters to infer the field induced by the Gulf Stream and Kurosio</p> <p>Three files "search_algorithm...." are the SWARM satellites data on regular grid. Again, Letter "A" in the name is for Alpha satellite, letter "C" is for Charlie satellite, letters "AC" denote combined two-satellite dataset. In Matlab/Octave the files can be read with a simple command <strong>load "name"</strong>. Each file containes the following fields: bz, by, bx, sig, nem, lat, lon which are three components of the magnetic fields, the standard deviation, number of data found by the search algoritn in each grid node, latitude and longitude. To check the data do the following: <strong>[X,Y]=meshgrid(lon,lat); contourf(X,Y,bz,10); colorbar</strong></p> <p>The land file containes two coordinates (xcst,ycst) of land masses. Command to read: <strong> load land</strong></p> <p>A file M025Mask is a mask function, see (Golubev, 2012) <strong>load M025mask</strong></p> <p>Kurosio_axis & Gulf_strea_axis are files containing coordinates of current's axis</p> <p>Figure 1,2,3 are .jpg files from the paper which is going to be submited to GRL: Yury Golubev: Magnetic pictures of the Gulf Stream and Kurosio as they seen by SWARM satellites</p> <p> </p>
GeaVR-tailored Immersive Virtual Scenario for the Theistareykir Fissure Swarm, Northern Iceland
<p>The dataset regards a virtual scenario designed to work with GeaVR software (https://geavr.eu/).</p><p><i><strong>Area of interest</strong></i>: the western portion of the emerging mid-ocean ridge in Northern Iceland, especially in the so-called Theistareykir Fissure Swarm.</p><p><i><strong>Aerial extent</strong></i>: 12.6 x 10.2 km.</p><p><i><strong>Texture resolution</strong></i>: 67.4 cm/pixel. </p><p><i><strong>Type of scenario</strong></i>: derived from photogrammetry-processing – historical aerial photos acquired in 1982 A.D..</p><p><i><strong>Please cite:</strong></i> Tibaldi, A., Bonali, F.L., Vitello, F. <i>et al.</i> Real world–based immersive Virtual Reality for research, teaching and communication in volcanology. <i>Bull Volcanol</i> <strong>82</strong>, 38 (2020). https://doi.org/10.1007/s00445-020-01376-6</p>
Data from: Genetic analyses reveal hybridization but no hybrid swarm in one of the world's rarest birds
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Data from: Discordant introgression in a rapidly expanding hybrid swarm
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Data from: Patterns of mating and generation of diversity in a Geum hybrid swarm
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Self-organization and information transfer in Antarctic krill swarms
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Data from: Do small swarms have an advantage when house hunting? The effect of swarm size on nest-site selection by Apis mellifera
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Data from: Testing the limits of pheromone stigmergy in spatially constrained robotic swarms
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Data from: Host plants of the non-swarming edible bush cricket Ruspolia differens
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.