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391
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Dataset results
391 results for “Roughness”
Evaluation of Root Roughness and Smear Layer Formation Using Conventional and Contemporary Dental Curettes
ClinicalTrials.gov study NCT04216966. IPD Sharing: NO. Countries: 1. Publications: 7.
Surface Roughness of a Dental Restorative Material and Biofilm Formation
ClinicalTrials.gov study NCT00256945. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Staying close to home? Genetic differentiation of rough-toothed dolphins near oceanic islands in the central Pacific Ocean
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Data from: Toxicity and population structure of the Rough-Skinned Newt (Taricha granulosa) outside the range of an arms race with resistant predators
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Data from: Searching for diamonds in the apomictic rough: a case study involving Boechera lignifera (Brassicaceae)
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Non-linear variation in clinging performance with surface roughness in geckos
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Phylogeography of the Rough Greensnake, Opheodrys aestivus (Squamata: Colubridae), using multilocus Sanger sequence and genomic ddRADseq data
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Attachment force (mN) of bed bugs Cimex lectularius males on Perspex (PMMA) in relation to surface roughness and wettability
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Data from: Evaluation of body mass index (BMI) as a prognostic indicator from two rough-toothed dolphin (Steno bredanensis) mass strandings in Florida
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Experimental flows through an array of emerged or slightly submerged square cylinders over a rough bed
<p>The experimental dataset was collected in an 18 m long and1 m wide laboratory flume.<br> An urbanised floodplain is modelled. The bed is rough, modelled with dense artificial grass. An array of square cylinders, representing housemodels, was set on the rough bed. The cylinder immersion rate was varied: cylinders are emerged for three flow cases<br> H/k = 42%, 93% and 98% (H water depth and k obstacle height) and slightly submerged for H/k = 148%.<br> This dataset comprises water, velocities across the channel and between y/(L/2) = 5 to 7 (L = 14.3 cm) measured using an Acoustic Doppler Velocimetry with a side looking probe, and velocities in longitudinal-vertical planes measured using Particle Image Velocimetry.</p> <p>This data set is explained in detail the following article :<br> Oukacine, M., Proust, S., Larrarte, F. <em>et al.</em> Experimental flows through an array of emerged or slightly submerged square cylinders over a rough bed. <em>Sci Data</em> <strong>8, </strong>6 (2021). <a href="https://doi.org/10.1038/s41597-020-00791-w">https://doi.org/10.1038/s41597-020-00791-w</a></p>
Response of flow and saltating particle characteristics to bed roughness and particle spatial density
<p>The data were used in the paper "Response of flow and saltating particle characteristics to bed roughness and particle spatial density" which was submitted to "Journal of Geophysical Research-Earth Surface". In this paper, the effects of bed roughness and particle spatial density on the bedload transport are investigated by numerical simulations. The distributions of key parameters for saltation, including their changes, are assessed using the PDF curves. It is found that bed roughness is likely composed of particles of equivalent size for fine particles.</p>
Optical topography of rough surfaces using vortex localization of fluorescent markers_experimental dataset
<p>This file contains the raw unprocessed experimental data for the results published in Petr Schovánek et al., Optical topography of rough surfaces using vortex localization of fluorescent markers, Optics Letters Vol. 45, Issue 16, pp. 4468-4471 (2020).</p>
Using generalized regression neural network to retrieve bare surface soil moisture from Radarsat-2 backscatter observations, regard less of roughness effect
<p>Description of soil moisture, correlation length, and RMS height from ground measurements of 147 sampling sites, full-polarized backscattering coefficients extracted from Radarsat-2 scene corresponding to those ground measurement sites.</p>
Data from: Adhesion force mapping on wood by atomic force microscopy: influence of surface roughness and tip geometry
This study attempts to address the interpretation of atomic force microscopy (AFM) adhesion force measurements conducted on the heterogeneous rough surface of wood and natural fibre materials. The influences of wood surface roughness, tip geometry and wear on the adhesion force distribution are examined by cyclic measurements conducted on wood surface under dry inert conditions. It was found that both the variation of tip and surface roughness of wood can widen the distribution of adhesion forces, which are essential for data interpretation. When a common Si AFM tip with nanometre size is used, the influence of tip wear can be significant. Therefore, control experiments should take the sequence of measurements into consideration, e.g. repeated experiments with used tip. In comparison, colloidal tips provide highly reproducible results. Similar average values but different distributions are shown for the adhesion measured on two major components of wood surface (cell wall and lumen). Evidence supports the hypothesis that the difference of the adhesion force distribution on these two locations was mainly induced by their surface roughness.
Influence of Roughness and Slip Velocity on the Evolution of Frictional Strength
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Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication - Dataset
<p>Data set used for the paper entitled "Multiscale modeling of lubrication between rough surfaces: Application to gas lubrication"</p> <p>The associated paper is archived on an open access aichive: https://hal.archives-ouvertes.fr/hal-03402682</p>
Figure 3 from: Ruas C, Vaz-dos-Santos AM (2017) Age structure and growth of the rough scad, Trachurus lathami (Teleostei: Carangidae), in the Southeastern Brazilian Bight. Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e20475
Figure 3 - Trachurus lathami: external surface of a left otolith showing measurements (OL = otolith length and OH = otolith height) and ring analysis (M and L are rings formed before the first annual one; more details in the text). Scale bar: 1 mm.
Figures 8-11 from: Ruas C, Vaz-dos-Santos AM (2017) Age structure and growth of the rough scad, Trachurus lathami (Teleostei: Carangidae), in the Southeastern Brazilian Bight. Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e20475
Figures 8-11 - Trachurus lathami: space-time distribution of age groups in the Southeastern Brazilian Bight during (8) summer of 2008 (January-February), (9) spring of 2008 (November), (10) spring of 2009 (September-October) and (11) summer of 2010 (February–March).
Figure 2 from: Ruas C, Vaz-dos-Santos AM (2017) Age structure and growth of the rough scad, Trachurus lathami (Teleostei: Carangidae), in the Southeastern Brazilian Bight. Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e20475
Figure 2 - Map of the Southeastern Brazilian Bight (SEBB) showing the locations of fishing hauls with Trachurus lathami catches by period (n = 17, some points are overlapped in the map).
Figures 5-6 from: Ruas C, Vaz-dos-Santos AM (2017) Age structure and growth of the rough scad, Trachurus lathami (Teleostei: Carangidae), in the Southeastern Brazilian Bight. Zoologia 34: 1-11. https://doi.org/10.3897/zoologia.34.e20475
Figures 5-6 - Trachurus lathami: (5) Box plot of ring radius (whiskers = minimum and maximum, box = interquartile range, bar = mean). (6) Constancy graph showing the position of rings radius (R) against total length (TL) (n = 278).
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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.
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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.