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2,911 results for “dispersal”
Fig. 3 in Systematics of the enigmatic South American Streblopus Van Lansberge, 1874 dung beetles and their transatlantic origin: a case study on the role of dispersal events in the biogeographical history of the Scarabaeinae (Coleoptera: Scarabaeidae)
Fig. 3. Streblopus punctatus (Balthasar, 1938). A‒B. Holotype, ♀. A. Dorsal view. B. Attached labels. C‒D. Ordinary specimens, dorsal view. C. ♂. D. ♀.
London Dispersion Governs the Interaction Mechanism of Small Polar and Non-Polar Molecules in Metal-Organic Frameworks
<p>Raw data set relating to publication.</p>
Influence of Paleolithic Range Contraction, Admixture and Long-Distance Dispersal on Genetic Gradients of Modern Humans in Asia
<p>Each folder is identified according to the scenario, and contains another folder with the input files (files *.txt, *.par, *.sam, *.asc) to simulate it, the corresponding simulated genetic data (files *.arp) and the derived PC maps (files *.png). A file with the locations of the samples is also included (coord.txt).</p> <p>* Pure Paleolithic expansion * <br> The folder “Paleo” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion, ignoring the range contraction induced by the LGM and LDD events.</p> <p>* Pure Paleolithic expansion considering the range contraction induced by the LGM * <br> The folder “Paleo_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion suffering the range contraction induced by the LGM.</p> <p> * Pure Paleolithic expansion considering long-distance dispersal (LDD) events * <br> The folder “Paleo_LDD” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a pure Paleolithic expansion considering LDD events. </p> <p>* Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo2NeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and later from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by two Neolithic expansions (IR=0) from Middle East and East Asia considering LDD events * <br> The folder “Paleo2NeoIR0_LDD” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by two Neolithic expansions from Middle East and later from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering the range contraction induced by the LGM * <br> The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from Middle East considering LDD events * <br> The folder “Paleo_MiddleEastNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0) from Middle East considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES) the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0) from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering the range contraction induced by the LGM * <br> The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering the range contraction induced by the LGM * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia suffering the range contraction induced by the LGM. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from Middle East considering LDD events * <br> The folder “Paleo_MiddleEastNeoIR004_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events. </p> <p>* Paleolithic expansion followed by a single Neolithic expansion (IR=0.04) from East Asia considering LDD events * <br> The folder “Paleo_EastAsiaNeoIR0_REC” contains the input files (INFILES), the genetic data and the corresponding PC maps simulated under the scenario of a Paleolithic expansion followed by a single Neolithic expansions (IR=0.04) from East Asia considering LDD events.</p>
Dataset and R-script for simple mechanistic model of Heracleum sosnowskyi seed dispersal by wind
<p>The dataset contains:</p> <p>- primary data about Heracleum sosnowskyi seeds traits (terminal velocity, mass, area, wing loading) and release heights for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions;</p> <p>- results of experiments of model seeds launches under different wind speeds;</p> <p>- R script for exploratory statistical analysis, linear regressions and mechanistc models testing.</p> <p>The anemochorous seed dispersal was generalized with a number of empirical and mechanistic models of varying complexity. The aim of this work was to develop the simplest possible mechanistic model of <em>Heracleum sosnowskyi</em> that allows to determine the distance of seed dispersal by wind with an accuracy comparable to that of empirical measurements. We measured and compared the characteristics of the seeds (terminal velocity, mass, area, wing loading) as well as the release height for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions. We tested two simplest mechanistic models: a ballistic model and a wind gradient model using identical artificial seeds with characteristics similar to those of real <em>H. sosnowskyi</em> seeds. The wind gradient model gave the best results, despite the fact that uniform in shape, weight and size artificial <em>H. sosnowskyi</em> seeds, when dropped simultaneously from the same height, fly off at different distances. This model provides an estimate of dispersal distances with an accuracy comparable to that of empirical measurements. We plan to use the presented model to develop an individual-based model that will allow us to calculate the flight distances of <em>H. sosnowskyi</em> propagules, taking into account real weather conditions in different years and in different parts of its invasion range. All primary data and R-scripts used are freely available at the Zenodo repository (https://doi.org/10.5281/zenodo.3766035).</p> <p> </p>
Numerical code and data for: Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon
<p>The numerical code and data accompanying the analysis of Figs. 4 and 5 of Suppressed Charge Dispersion via Resonant Tunneling in a Single-Channel Transmon, Phys. Rev. Lett. (2020)</p>
Figure 4 in Diversity and dispersal history of the talitrids (Crustacea: Amphipoda: Talitridae) of Bermuda
Figure 4. Driftwood photograph, consisting of spruce (Picea sp.) stranded in the wrack zone at Bailey's Bay on 8 April, 2014. Two individuals of Platorchestia monodi BOLD:AAB3402 were found within the Teredo burrows.
Figure 2 in Phoretic behaviour of Attacobius attarum (Roewer, 1935) (Araneae: Corinnidae: Corinninae) dispersion not associated with predation?
Figure 2. Behavioural repertoire of Attacobius attarum for dispersion in Atta sexdens: (A) female spider in bunch of loose soil from the nest of leaf-cutting ant, in search of a winged male; (B) approximation of female of leaf-cutting ants before the mating flight; (C) climbing of the spider to the dorsal region of winged female; (D) spider detail on the back of the queen; (E, F) winged male and female of leaf-cutting ants are preparing for the mating flight with phoretic spiders on their pronota.
Dataset and Simulation Files for article "Alumina coating for dispersion management in ultra-high Q microresonators"
<p>Link to arXiv submission: <a href="https://arxiv.org/abs/2009.07826">https://arxiv.org/abs/2009.07826</a></p> <ul> <li>Figure 1 <ul> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> <li>Data, Jupyter scripts, and simulations files for parts (f,g,h)</li> </ul> </li> <li>Figure 2 <ul> <li>Raw AFM data for use in Gwydeon software</li> <li>Data, Jupyter scripts, and comsol simulations files for parts (d,e)</li> </ul> </li> <li>Figure 3 <ul> <li>Raw data and Jupyter processing scripts for transmission spectrum calibration (parts - b)</li> <li>Frequency calibrated transmission spectrum and GVD/ Quality factor Jupyter scripts (parts d-g)</li> </ul> </li> <li>Figure 4 <ul> <li>Experimental data for parts a-g</li> </ul> </li> <li>Supplementary data <ul> <li>Raw ellipsometer data</li> <li>1D COMSOL FEM solver and analytical validation (including Julia Jupyter script)</li> <li>Quality factor estimation based on scattering and water adsorption</li> </ul> </li> </ul>
Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".
<p>Data and simulations files for the article "Quasinormal-mode perturbation theory for dissipative and dispersive optomechanics".</p>
Data from: Dispersal out of Wallacea spurs diversification of Pteropus flying foxes, the world's largest bats (Mammalia: Chiroptera)
<p><b>Aim: </b>Islands provide opportunities for isolation and speciation. Many landmasses in the Indo-Australian Archipelago (IAA) are oceanic islands, and founder-event speciation is expected to be the predominant form of speciation of volant taxa on these islands. We studied the biogeographic history of flying foxes, a group with many endemic species and a predilection for islands, to test this hypothesis and infer the biogeographic origin of the group.</p> <p><b>Location: </b>Australasia, Indo-Australian Archipelago, Madagascar, Pacific Islands</p> <p><b>Taxon: </b><i>Pteropus</i> (Pteropodidae)</p> <p><b>Methods: </b>To infer the biogeographic history of <i>Pteropus</i>, we sequenced up to 6169 bp of genetic data from 10 markers and reconstructed a multilocus species tree of 34 currently recognized <i>Pteropus</i> species and subspecies with 3 <i>Acerodon</i> outgroups using <span>BEAST</span> and subsequently estimated ancestral areas using models implemented in <span>BioGeoBEARS</span>.</p> <p><b>Results: </b>Species-level resolution was occasionally low because of slow rates of molecular evolution and/or recent divergences. Older divergences, however, were more strongly supported and allow the evolutionary history of the group to be inferred. The genus diverged in Wallacea from its common ancestor with <i>Acerodon</i>; founder-event speciation out of Wallacea was a common inference. <i>Pteropus </i>species in Micronesia and the western Indian Ocean were also inferred to result from founder-event speciation.</p> <p><b>Main conclusions: </b>Dispersal between regions of the IAA and the islands found therein fostered diversification of <i>Pteropus </i>throughout the IAA and beyond. Dispersal in <i>Pteropus</i> is far higher than in most other volant taxa studied to date, highlighting the importance of inter-island movement in the biogeographic history of this large clade of large bats.</p>
Quantifying dispersal variability among nearshore marine populations
<p>Project measuring variability in larval dispersal for clownfish <em>Amphiprion clarkii</em>. This repository is organized into 3 folders:</p> <ol> <li>code -This contains the R code to produce the results in the manuscript “Quantifying dispersal variability among nearshore marine populations”. My original coding was done in Jupyter Notebooks (IR Kernel). These notebooks are included in the folder "WorkingJupyterNotebooks", and can be viewed as HTML using the web site <a href="https://nbviewer.jupyter.org/">https://nbviewer.jupyter.org/</a>. The .R files were produced by downloading these notebooks as .R files.</li> <li>data -This contains the data to run the analysis using the code. The genomic data here is a filtered to use in Colony2. It also contains a backup of the SQL database with all data for the clownfish field and wetlab work in the Pinsky lab.</li> <li>genomics -This is the version of <a href="https://github.com/pinskylab/genomics">https://github.com/pinskylab/genomics</a> as it was at the time of this project. It contains all molecular wetlab metadata, raw genomic data, bioinformatics code, and protocols.</li> </ol> <p>Please contact Katrina at <a href="mailto:kat.catalano@rutgers.edu">kat.catalano@rutgers.edu</a> with any questions.</p>
Dataset on: Land slugs in plant nurseries, a potential cause of dispersal in Argentina
<p>Commercial plant nurseries may serve as causes of dispersal of land snails and slugs (native and non-native) through the trade of plants and the related transport of eggs and small individuals that may pass unnoticed. Studies on the possible role of plant nurseries as a potential cause of dispersal of slugs in South America are lacking. To explore the role of garden centers, we collected and identified slugs in 12 commercial nurseries in two cities in the province of Buenos Aires, Argentina. Eight species of slugs were found. Based on our findings we validate the existence of <em>Deroceras laeve</em> and <em>Belocaulus angustipes</em> for Argentina and confirm the existence of <em>Ambigolimax valentianus</em>, which was recently cited for Argentina. We recommend that plant nurseries be regularly monitored given that snail and slug species are accidentally spread through trade in plants.</p>
DEM simulations of bi-disperse beds during bedload transport
<p>This depository contains the data of all DEM simulations used in the publication Chassagne, R., Frey, P., Maurin, R., and Chauchat, J. Mobility of bidisperse mixtures during bedload transport. Physical Review Fluids, 5(11):114307. doi:10.1103/PhysRevFluids.5.114307, as well as post processing scripts to use the data.</p> <p>The simulations are located in seven folders, Monodisperse/ (mondisperse simulations where the fluid forcing is varied), N0.5/ (simulations with 0.5 layer of large particles above a small particle bed and or different fluid forcing), N1/ (simulations with 1 layer of large particles above a small particle bed and or different fluid forcing), N2/, N3/, N4/ and sizeRatio (2 layers of large particles, fixed fluid forcing but the diameter of the underlying small particles is varied). The data of each simulations are contained in separate subfolders named after the simulation. For example, H8Sh0.45/ corresponds to a monodisperse simulation with a bedheight of 8dl (dl is the large particle diameter) and a shields number of 0.45. H10N2R2Sh0.7/ corresponds to a bidisperse simulation with a bed height of 10dl, 2 layers of large particles, a size ratio of 2 between large and small particles and a shields number of 0.7. For each simulation, the time data are saved in data.hdf5 and averaged data in average.hdf5. A GeomParam.txt file is also in each folder. It contains information of the simulation that the post processing programm will read.</p> <p>The python script used to initiate the YADE-DEM simulation is also given for information (it contains all parameters of the simulation). The post-processing programm has been coded in python2.7 with an oriented-object procedure. The h5py package is necessary to read the .hdf5 files. The scripts do not work in python3, but can be very easily adapted if necessary (you only have to modify the "print" functions). The scripts are available in ScriptsPP/ and are organized as follow. For bidisperse simualtions, a mother class in SegregationPP and two child classes SegFull (to load the full time data set) and SegMean (to load only average data). For monodisperse simualtions, a mother class in MonodispersePP and two child classes MonoFull (to load the full time data set) and MonoMean (to load only average data). Two scripts examplePP1.py and examplePP2.py are proposed and show how to manipulate theses classes and the data.</p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Plant dispersal strategies of high tropical alpine communities across the Andes
<p>• Dispersal is a key ecological process that influences plant community assembly. Therefore, understanding whether dispersal strategies are associated with climate is of utmost importance, particularly in areas greatly exposed to climate change. We examined alpine plant communities located in the mountain summits of the tropical Andes across a 4000 km latitudinal gradient. We investigated species dispersal strategies and tested their association with climatic conditions and their evolutionary history.</p> <p>• We used dispersal-related traits (dispersal mode and growth form) to characterize dispersal strategies for 486 species recorded on 49 mountain summits. Then we analysed the phylogenetic signal of traits and investigated the association between dispersal traits, phylogeny, climate and space using structural equation modelling and fourth-corner analysis together with RLQ ordination.</p> <p>• A median of 36% species in the communities were anemochorous (wind-dispersed) and herbaceous. This dispersal strategy was followed by the barochory-herb combination (herbaceous with unspecialised seeds, dispersed by gravity) with a median of 26.3% species in the communities. The latter strategy was common among species with distributions restricted to alpine environments.</p> <p>• While trait states were phylogenetically conserved, they were significantly associated with a temperature gradient. Low minimum air temperatures, found at higher latitudes/elevations, were correlated with the prevalence of barochory and the herb growth form, traits that are common among Caryophyllales, Brassicaceae and Poaceae. Milder temperatures, found at lower latitudes/elevations, were associated with endozoochorous, shrub species mostly from the Ericaceae family. Anemochorous species were found all along the temperature gradient, possibly due to the success of anemochorous Compositae species in alpine regions. We also found that trait state dominance was more associated with the climatic conditions of the summit than with community phylogenetic structure. Although the evolutionary history of the tropical Andean flora has also shaped dispersal strategies, our results suggest that the environment had a more predominant role.</p> <p>• Synthesis: We showed that dispersal related traits are strongly associated with a gradient of minimum air temperatures in the Andes. Global warming may weaken this key filter at tropical alpine summits, potentially altering community dispersal strategies in this region and thus, plant community structure and composition.</p>
FIGURE 30 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 30. Bayesian tree (TPM 2 uf + G) for Aegla species based on partial fragment of 16 S. Node numbers represent posterior probabilities (values <50 % are not shown), and divergence time in millions of years (my); * indicates the calibration points to molecular clock. The clade C proposed by Pérez-Losada et al. (2004) is highlighted in grey. The basin and sub-basin origin of the discussed species in this study are shown after the specific names.
FIGURE 24. A – L in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 24. A – L, proximal portion of fifth pereiopod showing coxa and sexual tube of long and narrow type. A – B, Aegla paulensis Schmitt, 1942 s. str., male topotype (MZUSP 34368). C – D, Aegla rosanae Campos Jr., 1998, male topotype (MZUSP 34369). E – F, Aegla vanini n. sp., male paratype (MZUSP 34372). G – H, Aegla japi n. sp., male paratype (MZUSP 34375). I – J, Aegla jaragua n. sp. male paratype (MZUSP 34378). K-L, Aegla jundiai n. sp., male paratype (MZUSP 13490). Bars: A – D, F – H, J = 200 µm; K, L = 100 µm; E, I = 500 µm.
FIGURE 8 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 8. Types of Aegla Leach, 1820 male sexual tubes. A, long and narrow (A. lancinhas Bond-Buckup & Buckup in Santos et al., 2015, MZUSP 34403). B, short and wide (A. leptochela Bond-Buckup & Buckup, 1994, MZUSP 34491).
FIGURE 1 in Morphological and molecular data reveal the cryptic diversity among populations of Aegla paulensis (Decapoda, Anomura, Aeglidae), with descriptions of four new species and comments on dispersal routes and conservation status
FIGURE 1. Distribution of the species of Aegla in four main hydrographic basins of southern Brazil: Rio Grande, Rio Tietê (Upper Paraná system), Rio Paraíba do Sul and Ribeira de Iguape. Indications L 1 through L 7 refer to the locations mentioned under “ sampling area ” in the Material & Methods section.
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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)
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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.