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364 results for “Convection”
Slantwise Convection in the West Greenland Current (Source Code)
<p>The source code of two idealized experiments described in 'Slantwise Convection in the West Greenland Current'.</p>
Convection Enhanced Delivery of CSF in DBS for Parkinson's
ClinicalTrials.gov study NCT03540134. IPD Sharing: NO. Countries: 1. Publications: 1.
Convection-Enhanced Delivery (CED) of MDNA55 in Adults With Recurrent or Progressive Glioblastoma
ClinicalTrials.gov study NCT02858895. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
The Effect of Convective Pre-warming on Intra-operative Thermoregulatory Capabilities
ClinicalTrials.gov study NCT03876808. IPD Sharing: NO. Countries: 1. Publications: 12.
Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection
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Dissimilarity of turbulent transport of momentum and heat under unstable conditions linked to convective circulations
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Processed microphysical profiles of convective cloud scenes from satellite over ATTO
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PRECIP 2022 mei-yu front SAMURAI analyses, estimated rain rate, and convective-stratiform partition data
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AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold Networks
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Simulation details for: Radar signatures and surface observations of elevated convection associated with damaging surface winds
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Supporting materials and data from: Hypotheses concerning global magnetospheric convection, magnetosphere-Ionosphere coupling, and auroral activity at Uranus
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Data from: Physical mechanisms of deep convective boundary layer leading to dust emission in the Taklimakan desert
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Climate model output from a study of tropical cyclones over the Shanghai region under climate change based on a convection-permitting modelling
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Data from: Turbulence characteristics of ice-free radiatively driven convection in a deep, unstratified lake
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Supplementary videos for "Interplay Between Convective and Viscoelastic Forces Controls the Morphology of in vitro Paclitaxel- Stabilized Microtubules"
<p>Supplementary videos for MDPI Crystals manuscript "Interplay Between Convective and Viscoelastic Forces Controls the Morphology of in <em>vitro</em> Paclitaxel- Stabilized Microtubules "</p>
Type IIP Supernova Progenitors and Their Explodability. I. Convective Overshoot, Blue Loops, and Surface Composition
<p>MESA input and output files associated with the published work. We used MESA version r-10398 and MESA SDK version 20180822.</p> <p>The MESA_inputs.tar.gz contains work directories for each overshoot parameter (f) values analyzed in the paper. The models can be run directly from each working directory by making (./mk) and then running MESA (./rn). The modified run-script (rn) starts a MESA run from pre-main-sequence stage if the LOGS directory is absent, otherwise restarts from the latest photo saved in the photos directory. Each working directory has modified run_star_extras.f file that saves compactness parameters, mu_4, M_4 as extra history columns, as well as implements additional temporal resolution constraints as explained in our paper II (Wagle & Ray 2019, DOI:10.3847/1538-4357/ab5d2c, section 2.2).</p> <p>The f_*.tar.gz contains a history.data file (and corresponding binary file history.npy that can be directly loaded into python data structure), as well as several profile.data files and a saved model at CC stage for each 'f' value. It also contains png_plots generated by inlist_pgstar and the movies generated from these files using "images_to_movie.sh" script (available with MESA SDK).</p> <p> </p>
Statistical model training data for "Continuous Structural Parameterization: A proposed method for representing different model parameterizations within one structure demonstrated for atmospheric convection"
<p>Gzipped CSV files containing convection scheme inputs and outputs used for training.</p> <p>Column format of each file:</p> <p>THETA_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,Q_IN_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DTHETA_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,DQ_1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28</p> <p>where THETA_IN are input values of potential temperature [K], Q_IN are input values of specific humidity [kg/kg], DTHETA are changes in potential temperature due to convection [K], DQ are changes in specific humidity due to convection [kg/kg].</p> <p>Key:</p> <p>"llcs" are simulations with Lambert-Lewis.</p> <p>"gr" are simulations with Gregory-Rowntree.</p> <p>"4xco2" have 4 x pre-industrial atmospheric carbon dioxide concentration. (Others have 1 x pre-industrial atmospheric carbon dioxide concentration.)</p> <p>"rh0.7" and "rh0.9" have LLCS RHCRIT set to 70% and 90% respectively.</p> <p>All are 30 day simulations either for January "jan" or July "jul".</p> <p> </p>
Data for 'Robust Enhancement of Tropical Convective Activity by the 2019 Antarctic Sudden Stratospheric Warming'
<p>These data were used for making plots in the manuscript entitled 'Robust Enhancement of Tropical Convective Activity by the 2019 Antarctic Sudden Stratospheric Warming'. DOI: 10.1029/2020GL088743. Data format is NetCDF.</p>
Data from: Effects of soil particles and convective transport on dispersion and aggregation of nanoplastics via small-angle neutron scattering (SANS) and ultra SANS (USANS)
Terrestrial nanoplastics (NPs) pose a serious threat to agricultural food production systems due to the potential harm of soil-born micro- and macroorganisms that promote soil fertility and ability of NPs to adsorb onto and penetrate into vegetables and other crops. Very little is known about the dispersion, fate and transport of NPs in soils. This is because of the challenges of analyzing terrestrial NPs by conventional microscopic techniques due to the low concentrations of NPs and absence of optical transparency in these systems. Herein, we investigate the potential utility of small-angle neutron scattering (SANS) and Ultra SANS (USANS) to probe the agglomeration behavior of NPs prepared from polybutyrate adipate terephthalate, a prominent biodegradable plastic used in agricultural mulching, in the presence of vermiculite, an artificial soil. SANS with the contrast matching technique was used to study the aggregation of NPs co-dispersed with vermiculite in aqueous media. We determined the contrast match point for vermiculite was 66% D 2 O / 33% H 2 O. At this condition, the signal for vermiculite was ~50-100%-fold lower that obtained using neat H 2 O or D 2 O as solvent. According to SANS and USANS, smaller-sized NPs (50 nm) remained dispersed in water and did not undergo size reduction or self-agglomeration, nor form agglomerates with vermiculite. Larger-sized NPs (300-1000 nm) formed self-agglomerates and agglomerates with vermiculite, demonstrating their significant adhesion with soil. However, employment of convective transport (simulated by ex situ stirring of the slurries prior to SANS and USANS analyses) reduced the self-agglomeration, demonstrating weak NP-NP interactions. Convective transport also led to size reduction of the larger-sized NPs. Therefore, this study demonstrates the potential utility of SANS and USANS with contrast matching technique for investigating behavior of terrestrial NPs in complex soil systems.
Data from: A porous convection model for small-scale grass patterns
Spatial ecological patterns are usually ascribed to Turing‐type reaction‐diffusion or scale‐dependent feedback processes, but morphologically indistinguishable patterns can be produced by instabilities in fluid flow. We present a new hypothesis that suggests that fluid convection and chill damage to plants could form vegetation patterns with wavelengths ≈1–2 times the plant height. Previous hypotheses for small‐scale vegetation pattern formation relied on a Turing process driven by competition for water, which is thought to occur in large vegetation patterns. Predictions of the new hypothesis were consistent with properties of natural grass patterns in North Carolina, contradicting the Turing hypothesis. These results indicate that similarities in pattern morphology should not be interpreted as implying similarities in the pattern‐forming processes, that small‐wavelength vegetation patterns may arise from mechanisms that are distinct from those generating long‐wavelength vegetation patterns, and that fluid instabilities should be recognized as a cause of ecological patterns.
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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.