Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
391
datasets available to search
ShareScore release 0.9.0
Dataset results
391 results for “Roughness”
Figures of merit that characterize silicon gate-all-around nanowire FETs affected by line edge roughness variability
<p>Off-current, threshold voltage, sub-threshold slope and on-current values for two silicon gate-all-around nanowire FETs affected by line edge roughness (LER) variability, a 22 nm gate length device and a 10 nm gate length one. The LER profile that characterizes the roughness deformation is also included in the dataset. Different correlation length (CL) and root mean square (RMS) heights values are characterized.</p>
Figure 4 in First Field Collection of the Rough Sweetpotato Weevil, Blosyrus asellus (Olivier) (Coleoptera: Curculionidae), on Hawaii Island, with Notes on Detection Methods
Figure 4. Average (+ SEM) catch of adult rough sweetpotato weevils/trap/day in green light traps with and without added attractant(s). Treatments having the same letter above the error bar are not significantly different at the α = 0.05 level. Only results of treatments which included green light (Treatments 5–9) are presented here because there was no catch in this trial in traps of any of the treatments where the green light was off (Treatments 1–4).
Figure 2 in First Field Collection of the Rough Sweetpotato Weevil, Blosyrus asellus (Olivier) (Coleoptera: Curculionidae), on Hawaii Island, with Notes on Detection Methods
Figure 2. Locations on Hawaii island (marked with filled circles) where adult rough sweetpotato weevils have been recovered (map developed using ArcGIS [ESRI 2012]).
Figure 3 in First Field Collection of the Rough Sweetpotato Weevil, Blosyrus asellus (Olivier) (Coleoptera: Curculionidae), on Hawaii Island, with Notes on Detection Methods
Figure 3. Trap used for rough sweetpotato weevil detection ('Treatment 9'). (A) Flash picture taken at night to show the sweetpotato root section held underneath the green light and the water container inside the trap holding a sweetpotato leaf. (B) Picture taken without flash to show the appearance of the trap at night.
Disentangling developmental effects of play aspects in rat rough-and-tumble play
Open the record for dataset details and reuse information.
Effect of rough surface platforms on the mucosal attachment and the marginal bone loss of implants: A dog study.
<p>The preservation of peri-implant tissues is an important factor for implant success. This study aimed to assess the influence of the surface features of a butt-joint platform on soft-tissue attachment and bone resorption after immediate or delayed implant placement</p>
Rough russian yard
The real russian yard with addition from the author. That's a real place, you can check it here: https://goo.gl/maps/iYdwwFHiSj22 Source: Objaverse 1.0 / Sketchfab
Rough topography of modern Rome
This is a years-old capture of the Google Earth 3d mesh of modern Rome, which I have stretched vertically (quite a lot) and colored to make the differences in elevation more clear. I use this to communicate the basic topography of the city to students in my Roman art course, but the mesh is based on measurements of the *modern* city. You can see the low plane of the old Roman forum, but you can also see the modern Via dei Fori Imperiali that crosses it. The edge of the river, too, reflects the current embankments, instead of the more gradual ancient slope. Source: Objaverse 1.0 / Sketchfab
Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type
<p>Roughness is probably the most salient dimension pertaining to the perception of textures by touch and has been widely investigated. There is a controversy on how roughness relates to the texture’s spatial period and which factors influence this relation. Here, roughness during bare finger exploration of coarse textures is studied for different types of textures with elements of low height (0.3 mm). Participants were presented with square-wave gratings that were defined along one dimension and sine-wave gratings that were defined along one or two dimensions. Textures of each type varied in their spatial half period between 0.25 and 5.17 mm. Participants explored the textures by a lateral movement or a stationary finger contact. In all conditions judged roughness increased with spatial period up to a peak roughness and then decreased again. The exact function depended on the texture type, but hardly on exploration mode. We conclude that roughness is an inverted U-shaped function of texture period, if the textures are of low amplitude. The effects are explained by the interplay of two components contributing to the spatial code to roughness: variability in skin deformation due to the finger’s intrusion into the texture, which increases with the textures’ period up to a maximum (when the skin contacts the texture’s ground), and variability associated with the spatial frequency of the deformation, which decreases with spatial period.</p> <p><strong>Drewing</strong>, K. (2016). Low-Amplitude Textures Explored with the Bare Finger: Roughness Judgments Follow an Inverted U-Shaped Function of Texture Period Modified by Texture Type. <em>Haptics: Perception, Devices, Control, and Applications </em>(pp. 206-217). Springer: Heidelberg.</p> <p> </p> <p>The file DataPerTrialAndVp_Zenodo.txt contains all data relative to the publication.</p> <p>A description of the variables is contained in the file VARIABLE_CODES.txt</p>
Scan tiles obtained through Confocal Laser Scanning Microscopy for roughness characterization of surfaces
<p>The Data includes all the original data collected from individual scan tiles of both quartz and glass surfaces using Confocal Laser Scanning Microscopy. The techniques used for merging scan tiles linearly, as well as the procedures for data processing and analysis, are detailed in the methods and results sections of this manuscript. A sperate methods section include along with data files also describe the details of the Image acquisition, preprocessing, and tiling methodology.</p>
Data from: Spectral wear modelling of rubber friction on a hard substrate with large surface roughness
<p>Soft-hard matter friction is a long-standing tribology problem that remains unclarified, requiring engineers to empirically predict the wear life. To clarify this issue, this study examines the transient running-in regime of rubber friction on a hard rough substrate and models the temporal wear progression using the spectrum curves of surface roughness for both materials. Performing a series of friction tests and three-dimensional surface-height measurements, the time-dependent behaviours of the power spectral densities (PSDs) are divided into two phases, namely the initial non-steady and long-term steady phases. The detailed spectral analyses of worn rubber surfaces in the initial phase lead to a blended PSD function between self-affine and K-correlation surface models, consisting of one variable (the Hurst exponent) that is saturated by the substrate self-affinity. Supported by the Greenwood–Williamson theory concerning rough contact mechanics, the volumetric estimate with the blended PSD function is used to assess the volume rate of wear debris in the steady phase, which is validated experimentally. These findings not only improve the wear predictions of soft materials from previous measurements of worn surfaces but also help clarify the constrained multiscale mechanism of wear.</p>
Rough Shield
Shield done for first project back at uni. Had to loosely follow given concept art in shape, but we were allowed to texture it however we pleased. Source: Objaverse 1.0 / Sketchfab
Viking Longships Rough Seas in View Master
All in fun. Decided to do a diarama of two viking longships in rough seas, set in the style of an old view master some of us had as kids. Yes I am old haha. Enjoy! Source: Objaverse 1.0 / Sketchfab
Flat thin films forward simulations (Complex fresnel rs and rp). Rough thin film of sio2, sweeped in thickness about 1micron (Intensity and raw rs and rp.) Both as measured with 0 to NA = 0.9 angles.
<p>This dataset is part of a project under the ATMOC consortium, involving case studies of various simple one-material layers and their optical properties, particularly focusing on their interaction with light in terms of reflectivity. It encompasses samples , studied by institutions like UT Twente, IMEC, and CEA. These samples include flat surfaces coated with materials like Si, Mo, Ru, Al, Ru, Ni, Mo, Nb, Pt, Ta, Cr, TiN, and and rough thermal SiO2 on Si wafers, with thicknesses ranging from a few nanometers to several micrometers, applied through processes like PVD deposition and thermal oxidation at high temperatures.</p> <p>The dataset leverages refractive index data from the online resource "refractiveindex.info" to simulate the optical properties of these layers. The reflection of light is primarily determined by the Fresnel reflection coefficient, with additional simulations for rough surfaces using JCMWave's Finite Element Method (FEM) to understand the effects of surface roughness on light reflection. Primary setup it represents is prototype of Coherent Fourier Scatterometry (typically used for critical parameters of grating reconstruction|).</p> <p>Data are stored in .mat and .csv formats, with MATLAB scripts for generating visualizations of the model results, such as pcolor plots of intensity reflections based on variables like cover thickness and k-axis normalization. Python examples are also provided for loading and visualizing the data. The datasets aim to offer a comprehensive understanding of the optical properties of these materials and structures, supporting the development of standards and models for optical measurements and applications. Additionally, the dataset includes references to further studies and methodologies, such as the T-matrix approach and the Fabry-Perot formula extension, indicating a scholarly foundation in optical physics and engineering research.</p>
TVC Experiment 2018/19: LiDAR processed soil roughness
<p><span>This dataset contains the processed LiDAR data of Lange et al. (2021) (https://doi.pangaea.de/10.1594/PANGAEA.934387), as part of Environment and Climate Change Canada's 2018-2019 Trail Valley Creek Snow Experiment (TVC Experiment 18/19). These measurements were collected to evaluate soil roughness impacts on airborne and satellite radar measurements to better understand snow-radar interactions in a tundra environment. The measurements were around the Trail Valley Creek research station (https://www.trailvalleycreek.ca/) in Northwest Territories, Canada. Three periods of ground measurement took place in November 2018, January 2019, and March 2019. The dataset consists of LiDAR estimates of surface roughness statistics including the Root Mean Square (RMS) height and correlation length for a 100 m footprint around all in situ snowpit survey sites.</span></p>
Experimental data associated with publication "Small-scale roughness entraps water and controls underwater adhesion"
<p>This folder contains:</p> <ol> <li>Experimental adhesion data and the JKR Fit parameters.</li> <li>Raw SFG data used to create Figure 2.</li> <li>Power spectral densities of the 4 rough diamond surfaces. </li> <li>Summary sheets containing results for dry and underwater adhesion measurements. </li> </ol>
Landslide location, surface roughness, and age for the Teanaway basin, USA
<p>Excel database of mapped landslides in the Teanaway basin, Washington State, US. Landslides were mapped from 1m lidar provided by Quantum Spatial and publicly available at https://lidarportal.dnr.wa.gov/. The surface roughness was calculated with the MAD metric available at https://github.com/cageo/Trevisani-2015. </p>
Dataset for "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange"
<p>This dataset contains the sediment models, DNS flow field data, subsurface path data, and calculated transit time distributions for both the regular- and random-interface cases used in the paper: "Quantifying the effects of bed roughness on transit time distributions via direct numerical simulations of turbulent hyporheic exchange" by Guangchen Shen, Junlin Yuan, and Mantha S. Phanikumar (Submitted to Water Resources Research). <br> Detailed introduction of each data file is as follows.</p> <p>1. DNS flow field data</p> <p>Flowfield_Reg.h5 and Flowfield_Ran.h5 contains the following fields for the regular and random cases, respectively. 'ni', 'nj', 'nk' are the numbers of grid points along x, y, and z directions. 'xc', 'yc', 'zc' are the cell center locations. 'u3d','v3d','w3d' are the three-dimensional time-averaged velocities at each grid point. 'vof' is the volume-of-fluid field used by the immersed-boundary method to prescribe the fluid-solid interface (vof=1 in fluid and 0 in solid), at each grid point. 'vof' contains the information of sediment grain distribution and bed roughness geometry. Only the subsurface data (those below the sediment crest) are shared due to dataset size limit.</p> <p>2. Particle-tracked subsurface flow paths and corresponding transit time distributions</p> <p>The mat files “xxx_pathline” store the (x,y,z) location of each point (saved as 'StrX', 'StrY, 'StrZ') along the subsurface paths, discretized by uniform steps of travel time (with time intervals of 0.1 for 'A' and 0.01 for 'MD' cases, normalized by channel height and friction velocity). The files “xxx_TT” store the array of transit times corresponding to the tracked paths, where the 1d array T is the transit time. 'A' denotes calculations based on time-mean advection only, while 'MD' denotes calculations accounting for additional molecular diffusion. 'Interface' and '3DiameterBelow' indicate that the particles were released at the interface and -3 D below the interface, respectively.</p>
Modelling the transmission component in TIR reflectance spectra of sandstones to understand the effect of surface roughness and clinging fines
<p>This dataset includes a model that combines rock surface reflection with transmission through clinging fines the surface. All IDL scripts are provided. The dataset includes the transmission input spectra, raw data of transmission measurements and SEM images of the surfaces. The reflectance spectra presented in the paper are part of a previous publication, see related identifiers for database of this dataset.</p>
Surface roughness of Mercury polar deposits
<p>Supporting dataset.</p> <p> </p> <p>Roughness metrics for 5 north polar craters on Mercury derived from 125 mpp DEMs using a 3x3-cell moving window. See full text for derivation details.</p>
ScienceDex guides
Understand access before you commit
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.