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76 results for “Fast response”
MRI data of 40 adult participants in response to a cue induced craving task following food fasting, social isolation and baseline (within-subject design)
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Dataset for the ``Fast atmospheric response to a cold oceanic mesoscale patch in the north-western tropical Atlantic" publication
<p>The dataset presented here contains the files needed to produce the results presented in the publication "Fast atmospheric response to a SST mesoscale cold patch in the north-western subtropical Atlantic" submitted to the <em>Journal of Geophysical Research: Atmospheres</em>. The scripts that read and produce these files are publicly available at <a href="https://github.com/ClauClouds/SST-impact/">https://github.com/ClauClouds/SST-impact/</a> and can also be found in this repository (code_python.zip). This Zenodo data repository includes the following datasets:</p> <ul> <li> <p>Radiosonde data from 2-3 February 2020 (Stephan et al., 2021)</p> </li> <li> <p>Doppler lidar, and ARTHUS Raman lidar variables data from 2-3 February 2020,</p> </li> <li> <p>GOES-East (Geostationary Operational Environmental Satellite - East) Binary Cloud Mask (BCM) and Cloud Optical Depth (COD) products, provided at 2 km grid spacing every 10 minutes. They come from the GOES-R Advanced Baseline Imager (ABI) (Schmit et al., 2017), available at <a href="https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data">https://www.ncei.noaa.gov/products/satellite/goes-r-series.Data</a> and they are provided for the 2-3 February 2020.</p> </li> <li> <p>Multi-scale Ultra-high Resolution (MUR) product (JPL MUR MEaSUREs Project, 2015,183 (Chin et al., 2017)) averaged between the 2nd and 3rdfor the 2nd of February 2020. The MUR product is an analysis product provided on a daily basis that combines different satellite (infrared at high and medium resolutions and microwave products) and in-situ data (Chin et al., 2017).</p> </li> <li> <p>W-band radar data post-processed for the purposes of the publication. The original W-band radar data used are publicly accessible at <a href="https://howto.eurec4a.eu/merian_cloudradar.html">https://howto.eurec4a.eu/merian_cloudradar.html</a> and can be downloaded via <a href="https://eurec4a.aeris-data.fr/">AERIS data portal</a>. See more details and specific DOI below.</p> </li> </ul> <p>The present dataset is structured as follows:</p> <ul> <li> <p>diurnal_cycle_removed_vars: files containing the time series of the variables without noise and diurnal cycle (filenames with extended dates 20200202 and 20200203)</p> </li> <li> <p>diurnal_cycle: files containing the diurnal cycle of each variable used in the publication</p> </li> <li> <p>binned_sst_vars: files containing variables binned in terms of SST, used to derive the plots in the paper.</p> </li> <li> <p>satellite_data: a folder containing all satellite data used in the publication</p> </li> </ul> <p>Additional data used in the publication, that are processed via the scripts contained in the link mentioned above, are available online at the following urls:</p> <ul> <li> <p>cloud radar observations can be directly obtained from the public dataset identifiable via DOI: <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a> (Acquistapace et al., 2022)</p> </li> <li> <p>ASCAT wind field data and corresponding MUR SST data are available from the NASA JPL PODAAC platform (<a href="https://podaac.jpl.nasa.gov/">https://podaac.jpl.nasa.gov/</a>)</p> </li> <li> <p>hourly ERA5 (Hersbach et al., 2020) gridded fields (available at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form, last accessed March 2022) of the following variables: SST, water vapor mixing ratio, air temperature, and horizontal wind components. </p> </li> </ul> <p><br> </p> <p>References;</p> <p>Acquistapace et al., 2022, ESSD, <a href="https://doi.org/10.25326/235">https://doi.org/10.25326/235</a>.</p> <p>Schmit, T. et al., 2017, QJRMS, <a href="https://doi.org/10.1175/BAMS-D-15-00230.1">https://doi.org/10.1175/BAMS-D-15-00230.1</a></p> <p>Hersbach et al., 2020, QJRMS, <a href="https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803">https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.3803</a></p> <p>Stephan et al., 2021, ESSD, <a href="https://doi.org/10.5194/essd-13-491-2021">https://doi.org/10.5194/essd-13-491-2021</a></p> <p>Chin, T. M. et al., (2017), RS, <a href="https://doi.org/10.1016/j.rse.2017.07.029">https://doi.org/10.1016/j.rse.2017.07.029</a></p>
Fast-slow traits predict competition network structure and its response to resources and enemies
<p>Plants interact in complex networks but how network structure depends on resources, natural enemies, and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow-growing species responding differently. Nitrogen addition increased competition asymmetry in slow-growing networks, as expected, but decreased it in fast-growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understanding how competition responds to resources and enemies, a prediction from classic theories that has rarely been tested by linking functional traits to competition networks.</p>
Prosociality as response to slow- and fast-onset climate hazards
<p>This repository contains the information videos used in the treatments, as well as the data and code that replicates tables and figures for the following paper:<br> <strong>Title:</strong> Prosociality as response to slow- and fast-onset climate hazards<br> <strong>Authors:</strong> Ivo Steimanis<sup>1</sup> & Björn Vollan<sup>1,*</sup><br> <strong>Affiliations:</strong> <sup>1</sup> Department of Economics, Philipps University Marburg, 35032 Marburg, Germany<br> <strong>*Correspondence to:</strong> Björn Vollan <a href="mailto:bjoern.vollan@wiwi.uni-marburg.de">bjoern.vollan@wiwi.uni-marburg.de</a><br> <strong>ORCID:</strong> Steimanis: 0000-0002-8550-4675; Vollan: 0000-0002-5592-4185<br> <strong>Classification:</strong> Social Sciences, Economic Sciences<br> <strong>Keywords:</strong> climate hazards, prosociality, in-group favoritism, antisociality</p> <p> </p>
Dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"
<p>Reproducible dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"</p>
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
<p>Visual predators rely on fast-acting optokinetic responses to track and capture agile prey. Most toothed whales, however, rely on echolocation for hunting and have converged on biosonar clicking rates reaching 500/s during prey pu rsuits. If echoes are processed on a click by click basis, as assumed, neural responses 100x faster than those in vision are required to keep pace with this information flow. Using high resolution bio-logging of wild predator prey interactions we show that toothed whales adjust clicking rates to track prey movement within 50 200 ms of prey escape responses. Hypothesising that these stereotyped biosonar adjustments are elicited by sudden prey accelerations, we measured echo kinetic responses from trained harb our porpoises to a moving target and found similar latencies. High biosonar sampling rates are, therefore, not supported by extreme speeds of neural processing and muscular responses. Instead, the neuro kinetic response times in echolocation are similar to those of tracking responses in vision, suggesting a common neural underpinning.</p>
Echolocating toothed whales use ultra-fast echo-kinetic responses to track evasive prey
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Fast-slow traits predict competition network structure and its response to resources and enemies
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Fast and exact single and double mutation-response scanning of proteins: data and results
<p>Data, data analysis code, and results accompanying the manuscript: <strong>Fast and exact single and double mutation-response scanning of proteins: </strong>https://doi.org/10.1101/2020.10.23.352955</p> <p>In addition, the code needed to generate the data must be downloaded from <a href="https://doi.org/10.5281/zenodo.4123149">https://doi.org/10.5281/zenodo.4123149</a> and put in a subfolder named mutenm/</p>
Data from: Bistable soft jumper capable of fast response and high take-off velocity
<p>In contrast to jumping robots made from rigid materials, soft jumpers composed of compliant and elastically deformable materials exhibit superior impact resistance and mechanically robust functionality. However, recent efforts to create stimuli-responsive jumpers from soft materials are limited in their response speed, take-off velocity, and travel distance. Here, we report a magnetic-driven, ultrafast bistable soft jumper that exhibits the highest jumping capability (jumping over 108 body heights with a take-off velocity of over 2 m/s) and the fastest response time (less than 15 ms) compared to previous soft jumping robots. The snap-through transitions between bistable states form a repeatable loop that harnesses the ultrafast release of stored elastic energy. Based on the dynamic analysis, the multimodal locomotion of the bistable soft jumper can be realized: the interwell mode of jumping and the intrawell mode of hopping. These modes are controlled by adjusting the duration and strength of the magnetic field, which endows the bistable soft jumper with robust locomotion capabilities. In addition, it is capable of jumping omnidirectionally with tunable heights and distances. To demonstrate its capability in complex environment, a realistic pipeline with amphibious terrain was established. The jumper successfully finished the simulative task of cleansing polluted water through the pipeline. The design principle and actuating mechanism of the bistable soft jumper can be further extended for other flexible systems.</p>
Expression of Longevity Genes in Response to Extended Fasting
ClinicalTrials.gov study NCT01059760. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Role of Leptin in the Neuroendocrine and Immune Response to Fasting
ClinicalTrials.gov study NCT00140231. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Data from: Bistable soft jumper capable of fast response and high take-off velocity
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Fast response of Amazon rivers to Quaternary climate cycles
<p>Data table of Amazon rivers. Data sources are described in section 2.2 of the accompanying manuscript.</p>
Fasting Predictors of OGTT and MMTT Response
ClinicalTrials.gov study NCT01998867. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Early Effect of Fasting on Metabolic, Inflammatory, and Behavioral Responses in Females With and Without Obesity
ClinicalTrials.gov study NCT03532672. IPD Sharing: NO. Countries: 1. Publications: 1.
Effects of Time-Restricted Fasting on the Postprandial Glycemic Responses
ClinicalTrials.gov study NCT05913635. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Molecular Pathways Related to Short-term Fasting Response
ClinicalTrials.gov study NCT04259879. IPD Sharing: Not stated. Countries: 1. Publications: 22.
Metabolic Response to 3-day Fast Versus Carbohydrate-free Diet in Type 2 Diabetes
ClinicalTrials.gov study NCT01469104. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Correlation Between Duration of Fasting and Response to Fluid Replenishment, Evaluated With Repeated Measures of VTI.
ClinicalTrials.gov study NCT03599973. IPD Sharing: UNDECIDED. Countries: 1. Publications: 8.
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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.