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638 results for “Oscillation”
Figure 1 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 1. Possible range expansion scenarios for Tolypeutes matacus during glacial (A) and interglacial (B) periods. Yellow arrows denote scenarios proposed by Soibelzon (2019), while pink arrows indicate scenarios suggested by results from Ferreiro et al. (2022). Green polygon shows IUCN's current species distribution (Noss et al. 2014), and red and blue dots indicate fossil records from Holocene and Pleistocene periods, respectively (sensu Ferreiro et al. 2022).
Figure 5 in Effects of Quaternary climatic oscillations over the Chacoan fauna: phylogeographic patterns in the southern three-banded armadillo Tolypeutes matacus (Cingulata: Chlamyphoridae)
Figure 5. Spatiotemporal diffusion analysis of lineages for Tolypeutes matacus showing time slices for (A) 1.6 Mya, (B) 1.07 Mya, (C) 580 kya, and (D) the present. Red lines represent branches of the MCC tree and blue-shaded areas: indicate 80% HPD uncertainty in the location of ancestral nodes.
Dataset: Probabilistic work extraction on a classical oscillator beyond the second law
<p>Data set for the article "Probabilistic work extraction on a classical oscillator beyond the second law" , currently published as a preprint on arXiv, and accepted in Physical Review Letters.</p> <p>The preprint and SM have been uploaded for convenience.</p> <p>Inside the compressed file, you will find several folders</p> <p>It contains:</p> <p> </p> <ol> <li><strong>/Exemple trajectories/</strong> <ul> <li> 10 folders named Protocol 0 , Protocol 1 .... each containing the raw data of the 100 first experiments for each protocol. Each protocol was repeated 3000 time to increase the statistics for the article. We provide a representative subset.</li> <li>The Python analysis code "readexperimentalfile.py" . It reads the data in the aforementioned files, and computes the initial potential, the final potential, and the work associated to each protocols. Optionnaly, it saves trajectories used create Fig3 and Fig 4.</li> </ul> </li> <li><strong>/Figures/</strong> <ul> <li>Fig 1 : The pdf file for Fig1. (stochastic2.pdf) as presented in the article. Other formats can be provided on demand.</li> <li>Fig 2: The pdf file for Fig2, Setup.pdf</li> <li>Fig 3: The python code used to create Fig3 , named "plotpotentiel.py". To use it, one need the parameters tuning the initial potential used in the fifth protocol 'fitdepart5.npy', the final potential 'fitfin5.npy' as well as the set of trajectories "zfin5.npy" and "zinit5.npy". All those files were created using the provided python code "readexperimentalfile.py" , and are already provided in the same folder.</li> <li>Fig4: The python code used to create Fig4, named "plottrajex.py". "ztotsubset5.npy" and "lambdasubset5.npy" are provided to directly plot z(t) and lambda(t) during the protocol. "fitdepart5.npy" and 'fitfin5.npy", also created using "readexperimentalfile.py", are needed. </li> <li>Fig5: a) Everything needed to plot the first part of Fig 5. using the python file "workdpdf.py". All the datas of the other experiments are provided, and one can also choose to plot the work probability density function for other protocols (by changing the 5 t o another number. b) "mainfig.py" creates the bottom part of Fig5.</li> </ul> </li> </ol>
Oscillating superflow in multicomponent Bose-Einstein condensates
<p>Two movies 1) "<a href="../api/records/12714022/draft/files/Oscillatorysuperflow_supp1.mov/content" target="_blank" rel="noopener noreferrer">Oscillatorysuperflow_supp1.mov</a>" shows superflow with an oscillating direction of flow around an elliptical potential while 2) "<a href="../api/records/12714022/draft/files/Superflow_supp2.mov/content" target="_blank" rel="noopener noreferrer">Superflow_supp2.mov</a>" shows superflow of a two-component condensate around the same elliptical potential. Simulation parameters are included in the movies and the accompanying manuscript. </p>
The datasets analysed in the manuscript "Decadal Relationship between Arctic SAT and AMOC Changes Modulated by the North Pacific Oscillation"
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raw data and simulation scripts for "Exotic states in a simple network of nanoelectromechanical oscillators"
<p>Presented are raw data and simulation python scripts used to create figures from the manuscript "Exotic states in a simple network of nanoelectromechanical oscillators". Data is formatted by {time, mag_1, phase_1, mag_2, phase_2, mag_3, phase_3, mag_4, phase_4, mag_5, phase_5, mag_6, phase_6, mag_7, phase_7, mag_8, phase_8} for 17 column data files, and {time, phase_1, phase_2, phase_3, phase_4, phase_5, phase_6, phase_7, phase_8} for 9 column data. The data is organized by Figure number within manuscript and supplementary information.</p>
Persistently active El Niño–Southern Oscillation since the Mesozoic
<p>Data generated in this study are archived here.</p>
Iterative quantum optimization of spin glass problems with rapidly oscillating transverse fields
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Automatically deglitched dataset for "Searching the InSight seismic data for Mars's background free oscillations"
<p>The file Deglitched_sols.zip includes the dataset employed in the article "Searching the InSight seismic data for Mars's background free oscillations", published in<em> Seismological Research Letters.</em></p> <p>It includes 966 ".mseed" files, where each file corresponds to each automatically deglitched (SEISglitch + Twistpy) sol employed in the analysis. </p> <p>This dataset corresponds to the data depicted in Figure 6, and it was employed to produce Figure 8 (final results) of the manuscript. </p>
Interaction Between On-Chip Optical Parametric Oscillators: Towards Quantum States
<p>Repository for the project Interaction Between On-Chip Optical Parametric Oscillators: Towards Quantum States, funded by FAPESP under grant number 2022/06267-8</p>
Data and code for : Madden Julian Oscillation moves faster as the meridional moisture gradient intensifies in a warming world
<p>Data and code related to the research article titled “Madden Julian Oscillation moves faster as the meridional moisture gradient intensifies in a warming world,” published in Geophysical Research Letters.</p>
Active fluctuations of axoneme oscillations scale with number of dynein motors
<p> </p> <p>The dataset published here was used to measure the<strong> phase fluctuations</strong> <strong>(frequency jitter) </strong>of isolated and <strong>reactivated axonemes from <em>Chlamydomonas reinhardtii </em></strong>as a function of the dynein motor density in these axonemes. <br>The corresponding article is published in PNAS: https://doi.org/10.1073/pnas.2406244121</p> <p>Axonemes were isolated from wt(cc-125) and oda1(cc-2228) cells.</p> <p>Reactivation was performed:<br>(1) after dynein extraction with different amounts of salt (KCL) or<br>(2) in the presence of different concentrations of ATP .</p> <p>For all experimental conditions we provide the following data files:<br><br></p> <p><strong>Movies of reactivated axonemes:</strong></p> <p>Reactivated axonemes were imaged with phase-contrast microscopy. Recorded movies are organized in folders (.zip) which are labeled according to the respective reactivation condition (KCL and ATP concentration as well as the <em>Chlamydomonas</em> strain, from which the axonemes were purified, e.g. <strong>50mM_KCl_750uM_ATP_ODA_Extracted.zip</strong>).</p> <p>Those folders contain (1) movie files (multilayer .tif) of single axonemes and (.txt) files with a corresponding number-label (with microscope, camera settings and experimental condition (<em>Chlamydomonas</em> strain, [ATP], [KCL]).</p> <p>Using the number-label, the corresponding data file can be identified. <br><br></p> <p><strong>Data files (for the corresponding movies):</strong></p> <p>The experimental data is organized in MATLAB (.mat) files. Those files contain shape and waveform information (see below) for the respective reactivation condition (axoneme type, [ATP], [KCL] detailed below the file name).</p> <p><strong>WT_KCL_master.mat</strong> - WT axonemes, KCL extracted and reactivated with 750uM ATP</p> <p><strong>0mM_KCl_750uM_ATP_WT_Extracted<br>50mM_KCl_750uM_ATP_WT_Extracted<br>100mM_KCl_750uM_ATP_WT_Extracted<br>200mM_KCl_750uM_ATP_WT_Extracted<br>300mM_KCl_750uM_ATP_WT_Extracted<br>400mM_KCl_750uM_ATP_WT_Extracted<br></strong></p> <p><strong>WT_ATP_master.mat </strong> - WT axonemes reactivated with different ATP concentraions</p> <p><strong>0mM_KCl_50uM_ATP_WT<br>0mM_KCl_100uM_ATP_WT<br>0mM_KCl_370uM_ATP_WT<br>0mM_KCl_500uM_ATP_WT<br>0mM_KCl_750uM_ATP_WT<br></strong></p> <p><strong>ODA_KCL_master.mat </strong>- ODA axonemes, KCL extracted and reactivated with 750uM ATP</p> <p><strong>0mM_KCl_750uM_ATP_ODA_Extracted<br>50mM_KCl_750uM_ATP_ODA_Extracted<br>100mM_KCl_750uM_ATP_ODA_Extracted<br>200mM_KCl_750uM_ATP_ODA_Extracted<br>300mM_KCl_750uM_ATP_ODA_Extracted<br></strong></p> <p><strong>ODA_ATP_master.mat</strong> - ODA axonemes reactivated with different ATP concentraions</p> <p><strong>0mM_KCl_70uM_ATP_ODA<br>0mM_KCl_100uM_ATP_ODA<br>0mM_KCl_370uM_ATP_ODA<br>0mM_KCl_500uM_ATP_ODA<br>0mM_KCl_750uM_ATP_ODA</strong></p> <p>Each file includes a data-cell ‘Master’ with a columns for the different experimental conditions (e.g. concentrations of ATP or KCL). Data-cells contain one structure for each axoneme. </p> <p> </p> <p>These structures have the following fields:</p> <p>nframe… number of frames of the dataset</p> <p>phi_in_rad… phase angle (beat-cycle-phase) (rad)</p> <p>tangent_angle_psi_in_rad… the tangent angle for 24 positions along arc-length (rad)</p> <p>xy_in_micron… the x,y positions (µm)</p> <p>dt_in_second… time between adjacent frames (second)</p> <p>ds_in_micron… spacing between adjacent arc-length positions (µm)</p> <p>f0_in_Hz… beat frequency (Hz)</p> <p>A… beat amplitude (arc-length average) (rad)</p> <p>Q… quality factor</p> <p>sexp… string with experiment label</p> <p>KCL_in_mM… KCL concentration (mM) used for dynein extraction</p> <p>ATP_in_uM… ATP concentration (mM) used for reactivation</p> <p>File… filename, includes a number-label that corresponds to the imaging data provided</p>
Data and Code used in "Microphysics regimes due to haze-cloud interactions: cloud oscillation and cloud collapse"
<p>Data and Python Code for figure generation in "Microphysics regimes due to haze-cloud interactions: cloud oscillation and cloud collapse" submitted to Atmospheric Chemistry and Physics</p>
The history of the El Niño‒Southern Oscillation and sea surface salinity during 1376‒1500 CE reconstructed by Porites coral δ18O from Huangyan Island, South China Sea
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Unstable gas flow in a flat channel under the influence of a transverse force field: self-oscillations of the jet
<p>For a description of the task, see the original paper.</p> <div> <div> <div> <div>For each video, there are the meaning of force (F), Section and Subsection of the article where this video is mentioned, as well as the number of Figure from the article corresponding to this calculation. The Mach number is 0.29 for all the calculations.</div> </div> </div> </div> <p>(1) "Video-1.avi": </p> <p>F = 2.5, </p> <p>Section "Main modeling results"</p> <p>Subsection "Weak force field"</p> <p>Figure 2</p> <p> </p> <p>(2) "Video-2.avi": </p> <p>F = 4, </p> <p>Section "Main modeling results"</p> <p>Subsection "Strong force field"</p> <p>Figure 3</p> <p> </p> <p>(3) "Video-3.avi": </p> <p>F = 3, </p> <p>Section "Main modeling results"</p> <p>Subsection "Average force field"</p> <p>Figure 4</p> <p> </p> <p>(4) "Video-4.avi": </p> <p>F = 7 (continuous force field, see Figure 5), </p> <p>Section "Main modeling results"</p> <p>Subsection "Continuous force field"</p> <p>Figure 6</p>
Data from: Two-thirds of global cropland area impacted by climate oscillations
The El Niño Southern Oscillation (ENSO) peaked strongly during the boreal winter 2015-2016, leading to food insecurity in many parts of Africa, Asia and Latin America. Besides ENSO, the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) are known to impact crop yields worldwide. Here, we assess for the first time in a unified framework the relationship between ENSO, IOD and NAO and simulated crop productivity at the sub-country scale. Our findings reveal that during 1961–2010, crop productivity is significantly influenced by at least one large-scale climate oscillation in two-thirds of global cropland area. Besides observing new possible links – especially for NAO in Africa and the Middle East, our analyses confirm several known relationships between crop productivity and these oscillations. Our results improve the understanding of climatological crop productivity drivers, which is essential for enhancing food security in many of the most vulnerable places on the planet.
Data from: Getting there and around: host range oscillations during colonisation of the Canary Islands by the parasitic nematode Spauligodon
Episodes of expansion and isolation in geographic range over space and time, during which parasites have the opportunity to expand their host range, are linked to the development of host-parasite mosaic assemblages and parasite diversification. In this study we investigated whether island colonisation events lead to host range oscillations in a taxon of host-specific parasitic nematodes of the genus Spauligodon in the Canary Islands. We further investigated if range oscillations also resulted in shifts in host breadth (i.e. specialization), as expected for parasites on islands. Parasite phylogeny and divergence time estimates were inferred from molecular data with Bayesian methods. Host divergence times were set as calibration priors after a priori evaluation with a global-fit method of which individual host-parasite associations likely represent cospeciation links. Parasite colonisation history was reconstructed, followed by an estimation of oscillation events and specificity level. The results indicate the presence of four Spauligodon clades in the Canary Islands, which originated from at least three different colonisation events. We found evidence of host range oscillations to truly novel hosts, which in one case led to higher diversification. Contemporary host-parasite associations show strong host specificity, suggesting that changes in host breadth were limited to the shift period. Lineages with more frequent and wider taxonomic host range oscillations prior to the initial colonisation event showed wider range oscillations during colonisation and diversification within the archipelago. Our results suggest that a lineage's evolutionary past may be the best indicator of a parasite's potential for future range expansions.
Data for: "Possible link between decadal variability in precipitation in the South China Sea and the North Atlantic Oscillation during the 20th century: A perspective from coral geochemical records"
<p>This dataset includes all the data for the paper "Possible link between decadal variability in precipitation in the South China Sea and the North Atlantic Oscillation during the 20th century: A perspective from coral geochemical records" by Cui et al.</p>
Organelle calcium-derived voltage oscillations in pacemaker neurons drive the motor program for food-seeking behavior in Aplysia
<p><span>An atypical neuronal pacemaker mechanism, based on rhythmic intracellular calcium store release in an identified pair of interneurons (B63) and resulting oscillation of the neurons' membrane potential, acts as an autonomous releaser for the irregular occurrences of the <span>motor program for food-seeking behavior</span> in Aplysia. The rhythmic variations of the membrane potential in B63 neurons were analyzed by Fast Fourier Transform (FFT) analysis in a cycle period bandwidth of 512 s to 8 s. The resulting power spectral density periodograms were used to identify oscillation periods of peak magnitude. The periodograms were computed from the FFT frequency spectrograms by converting the frequency band (in Hz) to its reciprocal, period (in secs). These analysis were performed in isolated buccal ganglia preparations bathed in artifical sea water (ASW), in 'Low Ca+Co' saline to block chemical synapses, or in 'Low Ca+Co' saline and after an intracellular injection of organelle membrane calcium channel blocker heparin into either the bilateral B63 or B31 neurons. The oscillations of B63 membrane potential that were recorded in ASW persisted in 'Low Ca+Co', but were suppressed after heparin injection specifically into B63.</span></p>
Data from: Oscillayers: a dataset for the study of climatic oscillations over Plio-Pleistocene time scales at high spatial-temporal resolution
Motivation: In order to understand how species evolutionarily responded to Plio-Pleistocene climate oscillations (e.g. in terms of speciation, extinction, migration and adaptation), it is first important to have a good understanding of those past climate changes per se. This, however, is currently limited due to the lack of global-scale climatic datasets with high temporal resolution spanning the Plio-Pleistocene. To fill this gap, I here present Oscillayers, a global-scale and region-specific bioclim dataset, facilitating the study of climatic oscillations during the last 5.4 million years at high spatial (2.5 arc-minutes) and temporal (10 kyr time periods) resolution. This data set builds upon interpolated anomalies (Δ layers) between bioclim layers of the present and the Last Glacial Maximum (LGM) that are scaled relative to the Plio-Pleistocene global mean temperature curve, derived from benthic stable oxygen isotope ratios, to generate bioclim variables for 539 time periods. Evaluation of the scaled, interpolated estimates of palaeo-climates generated for the Holocene, Last Interglacial and Pliocene showed good agreement with independent General Circulation Models (GCMs) for respective time periods in terms of pattern correlation and absolute differences. Oscillayers thus provides a new tool for studying spatial-temporal patterns of evolutionary and ecological processes at high temporal and spatial resolution. Main types of variable contained: 19 bioclim variables for time periods throughout the Plio-Pleistocene. Input data and R script to recreate all 19 bioclim variables. Spatial location and grain: Global at 2.5 arc-minutes (4.65 x 4.65 = 21.62 km2 at the equator). Time period and grain: The last 5.4 million years. The grain is 10 kyr (= 539 time periods). Level of measurement: Data are for terrestrial climates (excluding Antarctica) taking sea level changes into account. Software format: All data are available as ASCII (ESRI) grid files.
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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.