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545 results for “synchronization”
Data from: The founding of Mauritian endemic coffee trees by a synchronous long-distance dispersal event
The stochastic process of long-distance dispersal is the exclusive means by which plants colonize oceanic islands. Baker's rule posits that self-incompatible plant lineages are unlikely to successfully colonize oceanic islands because they must achieve a coordinated long-distance dispersal of sufficiently numerous individuals to establish an outcrossing founder population. Here, we show for the first time that Mauritian Coffea species are self-incompatible and thus represent an exception to Baker's rule. The genus Coffea (Rubiaceae) is composed of approximately 124 species with a paleotropical distribution. Phylogenetic evidence strongly supports a single colonization of the oceanic island of Mauritius from either Madagascar or Africa. We employ Bayesian divergence time analyses to show that the colonization of Mauritius was not a recent event. We genotype S-RNase alleles from Mauritian endemic Coffea, and using S-allele gene genealogies, we show that the Mauritian allelic diversity is confined to just seven deeply divergent Coffea S-RNase allelic lineages. Based on these data, we developed an individual-based model and performed a simulation study to estimate the most likely number of founding individuals involved in the colonization of Mauritius. Our simulations show that to explain the observed S-RNase allelic diversity, the founding population was likely composed of fewer than 31 seeds that were likely synchronously dispersed from an ancestral mainland species.
Synchronized reagent delivery in double emulsions for triggering chemical reactions and gene expression
<p>Data underlying the figures in the publication “Synchronized reagent delivery in double emulsions for triggering chemical reactions and gene expression”, published in Small Methods.</p> <p>Table of contents:</p> <p>1. Figure 2C; Origin file containing all data and analysis for Figure 2C. Requires Origin Software to open.</p> <p>2. Figure 3; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure 3B and 3C. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>200721 FDG in PVA after prod_FDG sample 6_006.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 PVA shake_002.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 PVA shake_002.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>3. Figure 4BC; Origin file containing all data and analysis for Figure 4B and 4C. Requires Origin Software to open.</p> <p>4. Figure 5; Origin file containing all data and analysis for Figure 5D. Requires Origin Software to open.</p> <p>File Figure 5_201215-new LUVs 500 mM DH5alpha: FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure 5B. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 18h after prod_012.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 18h after prod- 05%SDS_018.fcs</p> <p>5. Figure S2; PDF files, report from DLS instrument.</p> <p>6. Figure S4; Excel file containing the values extracted from microscopy images for Figure S4.</p> <p>7. Figure S5; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure S5. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>200721 FDG in PVA after prod_FDG sample 6_006.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 5 SDS 01 shake_008.fcs</p> <p>200721 FDG in PVA&SDS 4 h after prod_FDG sample 6 SDS 0001 shake_006.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 4 SDS 001 shake_004.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 5 SDS 01 shake_008.fcs</p> <p>200721 FDG in PVA&SDS 22 h after prod_FDG sample 6 SDS 0001 shake_006.fcs</p> <p>8. Figure S6; Origin file containing all data and analysis for Figure S6 left-Requires Origin Software to open-, and Excel file containing the values from 96 well plate measurements used for Figure S6 right.</p> <p>9. Figure S7; Excel files containing the values from 96 well plate measurements used for Figure S7 left, and the values extracted from microscopy images for Figure S7 right.</p> <p>10. Figure S9; FlowJo Workspace for data analysis and fcs raw data acquired from flow cytometer for Figure S9. Requires FlowJo software to open.</p> <p>Raw data file names:</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 3h30min after prod_005.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 18h after prod_012.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 1 30min after prod_001.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 3h30min after prod - 05%SDS_008.fcs</p> <p>201215-new LUVs 500 mM-DH5alpha_sample 3 18h after prod- 05%SDS_018.fcs</p>
Data from: Transcriptome and proteome dynamics of a light-dark synchronized bacterial cell cycle
BACKGROUND: Growth of the ocean's most abundant primary producer, the cyanobacterium Prochlorococcus, is tightly synchronized to the natural 24-hour light-dark cycle. We sought to quantify the relationship between transcriptome and proteome dynamics that underlie this obligate photoautotroph's highly choreographed response to the daily oscillation in energy supply. METHODOLOGY/PRINCIPAL FINDINGS: Using Illumina RNA-sequencing transcriptomics and mass spectrometry-based quantitative proteomics, we measured timecourses of paired mRNA-protein abundances for 312 genes every 2 hours over a light-dark cycle. These temporal expression patterns reveal strong oscillations in transcript abundance that are broadly damped at the protein level, with mRNA levels varying on average 2.3 times more than the corresponding protein. The single strongest observed protein-level oscillation is in a ribonucleotide reductase, which may reflect a defense strategy against phage infection. The peak in abundance of most proteins also lags that of their transcript by 2-8 hours, and the two are completely antiphase for some genes. While abundant antisense RNA was detected, it apparently does not account for the observed divergences between expression levels. The redirection of flux through central carbon metabolism from daytime carbon fixation to nighttime respiration is associated with quite small changes in relative enzyme abundances. CONCLUSIONS/SIGNIFICANCE: Our results indicate that expression responses to periodic stimuli that are common in natural ecosystems (such as the diel cycle) can diverge significantly between the mRNA and protein levels. Protein expression patterns that are distinct from those of cognate mRNA have implications for the interpretation of transcriptome and metatranscriptome data in terms of cellular metabolism and its biogeochemical impact.
Synchronization processes of new breeders in a bird colony
<p>Data set for the Ms Synchronization processes of new breeders in a bird colony.</p>
Fig. 1 in Distribution of the Synchronous Flashing Beetle,Pteroptyx tener Olivier (Coleoptera: Lampyridae), in Malaysia
Fig. 1. Distribution of Pteroptyx tener in Malaysia. Inset: Location of Malaysia in Southeast Asia.
Microcomb-synchronized optoelectronics
<p>Dataset for paper "A microcomb-synchronized optoelectronic system"</p>
Data from: Synchronous effects produce cycles in deer populations and deer-vehicle collisions
<p>Population cycles are fundamentally linked with spatial synchrony, the prevailing paradigm being that populations with cyclic dynamics are easily synchronized. That is, population cycles help give rise to spatial synchrony. Here we demonstrate this process can work in reverse, with synchrony causing population cycles. We show that timescale-specific environmental effects, by synchronizing local population dynamics on certain timescales only, cause major population cycles over large areas in white-tailed deer. An important aspect of the new mechanism is specificity of synchronizing effects to certain timescales, which causes local dynamics to sum across space to a substantial cycle on those timescales. We also demonstrate, to our knowledge for the first time, that synchrony can be transmitted not only from environmental drivers to populations (deer), but also from there to human systems (deer-vehicle collisions). Because synchrony of drivers may be altered by climate change, changes to population cycles may arise via our mechanism.</p>
SURel: Synchronic Usage Relatedness
<p>This data collection contains synchronic semantic relatedness judgments for German word usage pairs drawn from general language and the domain of cooking. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>See previous versions for additional plots, tables and testsets.</p> <p>Please find more information on the provided data in the paper referenced below.</p> <p>Version: 3.0.0, 15.12.2021.</p> <p><strong>Reference</strong></p> <p>Anna Hätty, Dominik Schlechtweg, Sabine Schulte im Walde. 2019. <a href="https://aclanthology.org/S19-1001/">SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction</a>. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM). Minneapolis, Minnesota, USA, 2019.</p>
Sensorimotor Synchronization with Higher Metrical Levels in Music Shortens Perceived Time
<p>Data set for the study "Sensorimotor Synchronization with Higher Metrical Levels in Music Shortens Perceived Time" published in Music Perception.</p>
Supplementary material 1 from: Cheng S, Chan K-M, Ishak S-F, Khoo V, Chew MY (2017) Elucidating food plants of the aggregative, synchronously flashing Southeast Asian firefly, Pteroptyx tener Olivier (Coleoptera, Lampyridae). BioRisk 12: 25-39. https://doi.org/10.3897/biorisk.12.14061
Supporting Information S1. 16S ribosomal RNA sequence alignment : Data type: molecular data
Single-stage synchronous India-Asia collision model revealed by Himalayan high-pressure metamorphic rocks
Open the record for dataset details and reuse information.
ROV-Based Multi-Sensor Dataset: Synchronized Camera and Sonar images taken in the Tropical Waters of the Red Sea, Eilat
<p><strong>Description:</strong></p> <p>This dataset consists of approximately 46,928 synchronized image pairs collected by the Blue-ROV2. The images were captured using a machine-vision camera (IDS UI-3260CP-C-HQ) and a BluePrint Oculus M1200d Forward-Looking Sonar (FLS). Both sensors were installed with the FLS tilted 15 degrees downward to achieve optimal coverage of the terrain and optimal FOV overlap.</p> <p>The data was collected to train and evaluate a comprehensive perception and obstacle avoidance framework.</p> <p> </p> <p><strong>Context:</strong></p> <p>This dataset is the second installment in our collection of synchronized multi-sensor underwater datasets, aimed at enabling advanced research in multi-modal sensor fusion, obstacle detection, and navigation for autonomous underwater vehicles (AUVs). The data was collected using the Blue-ROV2 Remotely Operated Vehicle (ROV) in the tropical waters of the Red Sea, off the coast of Eilat, Israel. This data captures diverse underwater environments and is part of a research project focused on developing fusion models for improved obstacle detection and navigation in AUVs.</p> <p> </p> <p><strong>Content:</strong></p> <p>The data encompasses several sites within the tropical waters of the Red Sea, Eilat, including corals, rocks, shipwrecks, man-made structures, piers, and caves. The ROV platform was operated by divers, ensuring accurate positioning and coverage. Data was acquired at depths ranging from 3 to 12 meters at different times from dawn to dusk.</p> <p><strong> </strong></p> <p><strong>Dataset Composition:</strong><strong><br></strong></p> <div> <table> <tbody> <tr> <td> <p>Site</p> </td> <td> <p>Recording Session</p> </td> <td> <p>Image Pairs</p> </td> <td> <p>Description</p> </td> </tr> <tr> <td> <p>Tropical Site 1</p> </td> <td> <p>20221211_092506</p> <p>20221211_133252</p> </td> <td> <p>10,915</p> <p>7,978</p> </td> <td> <p>Pier, rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 2</p> </td> <td> <p>20221212_095821</p> <p>20221212_141308</p> </td> <td> <p>9,900</p> <p>8,475</p> </td> <td> <p>Man-made structure, </p> <p>rocks, corals</p> </td> </tr> <tr> <td> <p>Tropical Site 3</p> </td> <td> <p>20221213_102542</p> </td> <td> <p>9,390</p> </td> <td> <p>Rocks, corals</p> </td> </tr> <tr> <td> <p>Total</p> </td> <td> </td> <td> <p>46,928 </p> </td> <td> </td> </tr> </tbody> </table> </div> <p><strong> </strong></p> <p>The dataset is organized into separate sessions, each representing a specific dive or experiment. Within each session, data is further categorized into modalities: camera (FLC images), sonar (FLS images), and depth. Each modality directory contains the corresponding data files in PNG format for images and CSV format for depth data.</p> <p><strong> </strong></p> <p>Each modality directory includes:</p> <ul> <li> <p>A `camera.csv` file for the camera modality that maps each image file to its respective timestamp.</p> </li> <li> <p>A `sonar.csv` file for the sonar modality that maps each image file to its respective timestamp.</p> </li> <li> <p>The depth data in `depth.csv` formatted with `timestamp` and `value`.</p> </li> </ul> <p>Additionally, a `samples.json` file documents the relationship between uni-modal and multi-modal samples, enabling easy association of data from different modalities.</p> <p><strong> </strong></p> <p><strong>Technical Details:</strong></p> <ul> <li> <p>Camera: IDS UI-3260CP-C-HQ</p> </li> <ul> <li> <p>Image dimensions: 1936x1216 pixels (downscaled to 968 × 608 for this dataset)</p> </li> <li> <p>Sensor type: Sony IMX249 1/1.2" CMOS</p> </li> <li> <p>Lens: Tamron M112FM06</p> </li> <li> <p>Captured bit depth: 8-bit</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Sonar: BluePrint Oculus M1200d</p> </li> <ul> <li> <p>Operating frequency: 1.2 MHz (low frequency mode)</p> </li> <li> <p>Maximum range: 40 m (set to 15 m for this dataset)</p> </li> <li> <p>Horizontal aperture: 130°</p> </li> <li> <p>Vertical aperture: 20°</p> </li> <li> <p>Number of beams: 512</p> </li> <li> <p>Angular resolution: 0.6°</p> </li> <li> <p>Beam separation: 0.25°</p> </li> <li> <p>Image resolution: 544x300 pixels</p> </li> <li> <p>Coordinate system: Polar</p> </li> <li> <p>Frame rate: 5 Hz</p> </li> </ul> <li> <p>Depth: Blue-Robotics Ping2 Sonar Altimeter and Echosounder</p> </li> <ul> <li> <p>Frequency: 115 kHz</p> </li> <li> <p>Source Level: 198 dB re 1µPa @ 1m</p> </li> <li> <p>Beamwidth: 25 degrees</p> </li> <li> <p>Typical Minimum Range: 0.3 m (1 ft)</p> </li> <li> <p>Typical Usable Range: 100 m (328 ft)</p> </li> <li> <p>Range Resolution: 0.5% of range</p> </li> <li> <p>Depth Rating: 300 m (984 ft)</p> </li> <li> <p>Data format: CSV</p> </li> <li> <p>Columns:</p> </li> <ul> <li> <p>timestamp: Unix timestamp (seconds)</p> </li> <li> <p>value: Depth value (meters)</p> </li> </ul> <li> <p>Sample rate: 5 Hz</p> </li> </ul> </ul> <p><strong> </strong></p> <p><strong>Example File Tree Layout:</strong></p> <p>```<br>${session}/<br>${dataset}/<br>camera/<br>camera.csv<br>00000001.png<br>00000002.png<br>…<br>sonar/<br>sonar.csv<br>00000001.png<br>00000002.png<br>…<br>depth/<br>depth.csv<br>samples.json<br>```<strong> <br><br>Example File Content:</strong></p> <p><strong> </strong>camera.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>sonar.csv<br>```<br>timestamp,filename<br>1644234340.181234,00000001.png<br>1644234343.375667,00000002.png<br>```</p> <p><strong> </strong>depth.csv<br>```<br>timestamp,value<br>1644234340.181234,5.4<br>1644234343.375667,6.1<br>```</p> <p><strong> </strong>samples.json</p> <p>```<br>{<br> "samples": [<br> {<br> "camera": [<br> 0<br> ],<br> "depth": [<br> 0<br> ],<br> "sonar": [<br> 0<br> ]<br> },<br> {<br> "camera": [<br> 1<br> ],<br> "depth": [<br> 1<br> ],<br> "sonar": [<br> 1<br> ]<br> }<br>]</p> <p>```</p> <p>By providing synchronized and aligned camera, sonar imagery, and depth data, this dataset enables researchers to explore novel algorithms and techniques for multi-modal sensor fusion in the context of autonomous underwater vehicles operating in the tropical waters of the Red Sea.</p> <p><strong>Acknowledgements</strong></p> <p>The data in this repository is part of the DeeperSense project that received funding from the European Commission, Program H2020-ICT-2020-2 ICT-47-2020, Project Number: 101016958.</p>
Рис. 5. Суточная активность поΑземной поΛевки (на примере синхронной активности ♀ № 1 и № 3, ♂ № 4; июΛь 2000 г., манеж БиНИИ) Fig. 5. Daily activity of the common pine vole (on the example of synchronous activity of ♀ No. 1 and 3, ♂ No. 4; July 2000, measured in a pen at the Scientific Research Institute of Biology) in Spatial Organization Of Common Pine Vole (Microtus Subterraneus Selys-Longchamps, 1836) Colonies
Рис. 5. Суточная активность поΑземной поΛевки (на примере синхронной активности ♀ № 1 и № 3, ♂ № 4; июΛь 2000 г., манеж БиНИИ) Fig. 5. Daily activity of the common pine vole (on the example of synchronous activity of ♀ No. 1 and 3, ♂ No. 4; July 2000, measured in a pen at the Scientific Research Institute of Biology)
Self-Assembly and Synchronization: Crafting Music with Multi-Agent Embodied Oscillators - DATASET
<p>Dataset for amalysis replication</p>
Single-stage synchronous India-Asia collision model revealed by Himalayan high-pressure metamorphic rocks
Open the record for dataset details and reuse information.
Assessing the elevational synchronization in vegetation phenology across Northern Hemisphere mountain ecosystems under global warming
<p>The data and code supporting the results in this paper is provided.</p>
Data from: A quantitative theory of gamma synchronization in macaque V1
Gamma-band synchronization coordinates brief periods of excitability in oscillating neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared frequency over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous frequency modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma frequencies. When similar enough, these frequencies continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized frequency difference. Crucially, the precise dynamics of frequencies and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled oscillators influence each other's phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous frequency modulations applies to gamma in V1, and is likely generalizable to other brain regions and rhythms.
Data from: Predation can select for later and more synchronous arrival times in migrating species
For migratory species, the timing of arrival at breeding grounds is an important determinant of fitness. Too early arrival at the breeding ground is associated with various costs, and we focus on one understudied cost: that migrants can experience a higher risk of predation if arriving earlier than the bulk of the breeding population. We show, using both a semi-analytic and simulation model, that predation can select for later arrival. This is because of safety in numbers: predation risk becomes diluted if many other individuals, either con- or heterospecific, are already residing in the area. Predation risk dilution can also select for more synchronous arrival because deviating from the current population-wide norm to earlier or later dates leads to higher predation risk or to failures in territory acquisition, respectively. The fact that selection for high arrival synchrony can in some cases be more important than selection for a specific date (early or late) within the season is an example of an 'evolutionary priority effect': whichever strategy – in this case a particular arrival time – becomes established in a population can remain stable over long periods of time; there are many possible equilibria (multiple stable states) which the population can remain at. Mixed arrival strategies are also possible under some circumstances.
Activity synchronization and fission decisions
<p><span><span>Group-living animals need to reach a consensus to maintain cohesion. When the costs of consensus </span>outweigh the benefits, the group may (temporarily) split into two or more subgroups. Consensus can concern the activity to pursue or the direction of travel. Temporary group separation is a common feature in species with a high degree of fission-fusion dynamics. We investigated the role activity synchronization played in fission decisions in a spider monkey group living in the Otoch Ma'ax Yetel Kooh Nature Reserve, Yucatan, Mexico. For 21 months, we recorded every fission event occurring in the followed subgroup, as well as the subgroup activity. We classified the activity as "synchronized" when at least the 75% of subgroup members performed the same activity (resting, foraging, socializing or traveling); otherwise, we classified it as "non-synchronized". We found that fission events occurred more often when the activity was non-synchronized. In addition, when the activity was synchronized, fission events occurred more often when spider monkeys were traveling than when they engaged in other subgroup activities. Our findings highlight the role of consensus over the activity to pursue and the direction where to travel on fission decisions.</span></p>
Figure 4 from: Tanaka S (2021) Embryo-to-embryo communication facilitates synchronous hatching in grasshoppers. Journal of Orthoptera Research 30(2): 107-115. https://doi.org/10.3897/jor.30.63405
Figure 4 Hatching intervals of two eggs kept in contact with one another (top panel), separated by ~5 mm (middle panel) with or without a screen,or connected by a piece of wire (bottom panel) at 30°C under continuous illumination in the six indicated grasshopper species (A–F.). Different letters indicate significant differences in mean values at the 5% level by the Steel-Dwass test. Diagrams in panels show how the eggs were arranged in wells.
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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.