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89 results for “surface roughness”
MCR LTER: Data from Duvall, Rosman and Hench, in review. Representation of coral reef roughness using obstacle and surface-based approaches, submitted to JGR: Oceans
This archive contains natural coral reef topography data from the northern coast of Mo’orea, French Polynesia. These data were used to compute reef roughness density using obstacle- and surface-based estimates and models, and to compare the two approaches for representing reef topography. Primary support for this product came from the National Science Foundation Physical Oceanography program (OCE-1435530 and OCE-1435133), and as well as Duke University and the University of North Carolina at Chapel Hill. This material is based upon work supported by the U.S. National Science Foundation under Grant No. OCE 16-37396 (and earlier awards) as well as a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2019). This work represents a contribution of the Moorea Coral Reef (MCR) LTER Site.
Data and code for the publication "Tracing the horizontal transport of microplastics on rough surfaces"
<p><strong>Background</strong></p> <p>The data set contains images of fluorescent PMMA (Polymethyl methacrylate) particles that are moved by water on rough surfaces in an irrigation experiment. The experiments were done in the laboratory at the Institute of Geography, University of Cologne, Germany, in Septembre 2020. The images were taken with an sCMOS (advanced scientific complementary metal-oxide-semiconductor) high resolution pco.panda 4.2 camera (PCO AG, Kehlheim, Germany).</p> <p>The data set was analysed in the publication: Laermanns, H., Lehmann, M., Klee, M., Löder, M.G.J., Gekle, S. and Bogner, C., 2021, “Tracing the horizontal transport of microplastics on rough surfaces,” Microplastics and Nanoplastics, <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a></p> <p>Additionally to the data, this collection of files contains the Python and R scripts/notebooks used to analyse the images and create graphics for the publication. The code for the simulation of flow patterns can be obtained from the authors upon request.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data and code are provided as is without any warranty.</p> <p>Experimental parameters</p> <ul> <li> <p>Surface roughness: two levels, fine and course</p> </li> <li> <p>Inclination: 6 levels, 2.5°, 5°, 7.5°, 10°, 12.5° and 15°</p> </li> <li> <p>Irrigation: three levels, 4.8, 7.2 and 10.44 L/h</p> </li> <li> <p>Repetitions: three</p> </li> </ul> <p>More details on the experimental setup are given in the publication.</p> <p> </p> <p><strong>Description of the dataset</strong></p> <p>The folder <strong>images.zip</strong> contains the images. They are organized as follows:</p> <ul> <li><strong>Feinsand_10_Partikel</strong>: images of PMMA particles on the fine surface</li> <li><strong>Grobsand_10_Partikel</strong>: images of PMMA particles on the rough surface <ul> <li> <p>Both folders contain six subfolders <strong>_XX_Grad_Gefaelle</strong>, XX being 2_5, 5, 7_5, 10, 12_5, 15. These folders refer to inclinations of 2.5°, 5°, 7.5°, 10°, 12.5° and 15° of the rough surfaces, respectively.</p> </li> <li> <p>every folder _XX_Grad_Gefaelle contains three subfolders <strong>Fliessgeschwindigkeit_YY</strong>, with YY being 20mlx4, 30mlx4 and 43_5mlx4, the parameters of the peristaltic pump, corresponding to irrigation rates of 4.8, 7.2 or 10.44 L/h, respectively.</p> </li> <li> <p>every folder Fliessgeschwindigkeit_YY contains three subfolders <strong>Z_Durchgang</strong> with Z being 1, 2 or 3 corresponding to the tree repetitions of the experiment.</p> </li> </ul> </li> <li><strong>stained_flow_patterns</strong>: images of flow patterns of the fluorescent dye Nile Red (in methanol), an mp4 video and a text file with parameters to produce the video based on the images. The images were produced for the following experimental parameters: <ul> <li> <p><strong>Feinsand_2_5_Grad_20_ml</strong>: fine surface, inclined by 2.5° and irrigated with 7.2 L/h</p> </li> <li> <p><strong>Grobsand_7_5_Grad_20_ml</strong>: coarse surface, inclined by 7.5° and irrigated with 7.2 L/h</p> </li> </ul> </li> </ul> <p>The file <strong>experimental_data.csv</strong> links the concatenated folder names to experimental parameters.</p> <p> </p> <p><strong>Description of the code</strong></p> <p>The images were first processed in Python to locate the PMMA particles and calculate particle sizes. The Python code is located in the <strong>py_scripts.zip</strong> folder. It contains the following files:</p> <ul> <li> <p><strong>find_XYZ</strong>: locates PMMA particles. XYZ stands for different experimental parameters (see above). Scripts containing the string <strong>_problems</strong> locate PMMA particles for images with possible artefacts (smeared particles, residual light etc.). You need to uncomment the appropriate lines in the files to rerun the code because it was run piece by piece.</p> </li> <li> <p><strong>pickle_to_csv.py</strong>: converts pickle files to csv files</p> </li> <li> <p><strong>calculate_sizes.py</strong>: calculates the sizes of PMMA particles from the first image of each experiment</p> </li> <li> <p><strong>py_functions_new.py</strong>: contains custom functions</p> </li> </ul> <p>Further analysis run in a mixture of R and Pyhton in one working document (R Notebook):</p> <ul> <li> <p><strong>Analysis_with_loops.Rmd</strong>: tracking of the PMMA particles by PtrakPy version 0.4.2 (Allan et al. 2019). Python 3.8 (Python Software Foundation, <a href="https://www.python.org/">https://www.python.org/</a>) was called directly from R using the R package reticulate (<a href="https://rstudio.github.io/reticulate/">https://rstudio.github.io/reticulate/</a>) in RStudio (<a href="https://www.rstudio.com/">https://www.rstudio.com/</a>).</p> </li> <li> <p><strong>Analysis_for_paper.Rmd</strong>: R code for analysis of tracking, statistical analysis, plotting. We used the R version 4.0.3 (R Core Team 2020).</p> </li> <li> <p><strong>helper_function.R</strong>: contains custom R functions for the analysis</p> </li> </ul> <p> </p> <p><strong>Results</strong></p> <p>The file <strong>results.zip</strong> contains the folders:</p> <ul> <li> <p><strong>data</strong>: *.pickle files produced by Python containing the trajectories of PMMA particles</p> </li> <li> <p><strong>data_csv</strong>: *.pickle files converted to *.csv files</p> </li> <li> <p><strong>figures</strong>: figures produced by the code during the analysis, organized in different subfolders</p> </li> <li> <p><strong>RData</strong>: large computational results produced and saved during analysis</p> </li> <li> <p><strong>sizes_csv</strong>: *.csv files containing PMMA particle sizes and further morphological characteristics; produced during analysis</p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The authors thank Julia Horn for support in the laboratory and Florian Steininger for technical assistance.</p> <p> </p> <p><strong>Funding</strong></p> <p>This project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), Project Number 391977956, SFB 1357, subprojects B04 and B06.</p> <p> </p> <p><strong>References</strong></p> <p>Allan, Dan, Casper van der Wel, Nathan Keim, Thomas A Caswell, Devin Wieker, Ruben Verweij, Chaz Reid, et al. 2019. <em>Soft-Matter/Trackpy: Trackpy V0.4.2</em> (version v0.4.2). Zenodo. <a href="https://doi.org/10.5281/zenodo.3492186">https://doi.org/10.5281/zenodo.3492186</a>.</p> <p>Laermanns, Hannes, Moritz Lehmann, Marcel Klee, Martin GJ Löder, Stephan Gekle, and Christina Bogner. 2021. “Tracing the Horizontal Transport of Microplastics on Rough Surfaces.” <em>Microplastics and Nanoplastics</em>. <a href="https://doi.org/10.1186/s43591-021-00010-2">https://doi.org/10.1186/s43591-021-00010-2</a>.</p> <p>R Core Team. 2020. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.</p>
Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region
<p>The datasets related to the work of<em> Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang'e-4 Exploration Region.</em></p> <p>Cite the following references if using the DEM data. Wu, B., Li, Y., Liu, W. C., Wang, Y., Li, F., Zhao, Y., and Zhang, H. (2021), Centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang’E-4 landing site, Earth Planet. Sci. Lett., 553, 116666. <a href="https://doi.org/10.1016/j.epsl.2020.116666">https://doi.org/10.1016/j.epsl.2020.116666</a></p> <p>Guo, D., Fa, W., Wu, B., Li, Y., & Liu, Y. (2021). Millimeter- to Decimeter-Scale Surface Slope and Roughness of the Moon at the Chang’e-4 Exploration Region. <em>Geophysical Research Letters</em>, 48, e2021GL094931. <a href="https://doi.org/10.1029/2021GL094931">https://doi.org/10.1029/2021GL094931</a></p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Text-fig. 15. Scanning electron microscope (SEM) images of seeds of Pazliopsis reyi (a–c) and Anaspermum operculatum gen. et sp. nov. (d–j). a) Seed in lateral view showing poorly preserved remains of fruit wall and rough surface of the exotesta; b) Section through the wall of a broken seed showing palisade cells of the exotesta; c) Seed surface showing sunken very deeply sinuous outlines of the exotesta cells; d–f, h, i) Seeds in lateral view (holotype figured in d) showing the pointed micropylar-hilar area, the rounded chalazal end, the smooth surface of the exotesta composed of cells with sinuous cell outlines, and the distinct course of the raphe; g) Surface of the exotesta showing the distinctive cell outlines formed by the sinuous anticlinal walls of the palisade cells; j) Apical view of seed showing the prominent operculum. Specimens, TV43-S171534 (a), TV44-S136683 (b), TV43-S136745 (c), TV43-S136740 (holotype; d), TV43-S136746 (e), TV43-S136739 (f), TV43-S170083 (g), TV S136743 (h), TV43-S136742 (i), TV38-S174608 (j). Scale bars 300 Μm (a, b, d–f, h, i), 50 Μm (j), 30 Μm (c, g). in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 15. Scanning electron microscope (SEM) images of seeds of Pazliopsis reyi (a–c) and Anaspermum operculatum gen. et sp. nov. (d–j). a) Seed in lateral view showing poorly preserved remains of fruit wall and rough surface of the exotesta; b) Section through the wall of a broken seed showing palisade cells of the exotesta; c) Seed surface showing sunken very deeply sinuous outlines of the exotesta cells; d–f, h, i) Seeds in lateral view (holotype figured in d) showing the pointed micropylar-hilar area, the rounded chalazal end, the smooth surface of the exotesta composed of cells with sinuous cell outlines, and the distinct course of the raphe; g) Surface of the exotesta showing the distinctive cell outlines formed by the sinuous anticlinal walls of the palisade cells; j) Apical view of seed showing the prominent operculum. Specimens, TV43-S171534 (a), TV44-S136683 (b), TV43-S136745 (c), TV43-S136740 (holotype; d), TV43-S136746 (e), TV43-S136739 (f), TV43-S170083 (g), TV S136743 (h), TV43-S136742 (i), TV38-S174608 (j). Scale bars 300 Μm (a, b, d–f, h, i), 50 Μm (j), 30 Μm (c, g).
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>
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>
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>
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>
MFMET webinar - 03. Wettability and surface roughness
<p>This video presents measurements of surface properties in microfluidics. The measured quantities are wettability, surface energy, and surface roughness.</p> <p>Further reading can be found:<br> <br>Reference:<br>Deliverable 5 - Guidelines for the measurement of key performance parameters of microfluidic connections including the identification of key properties in an interface. <a href="https://doi.org/10.5281/zenodo.10731759">Link</a></p> <p><br>The project (20NRM02 MFMET) have received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
Dataset accompanying the publication "Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy"
<p>This dataset accompanies the following publication:</p> <p>Hülagü, D., Tobias, C., Dao, R., Komarov, P., Rurack, K., Hodoroaba, V.-D., Towards 3D determination of the surface roughness of core-shell microparticles as a routine quality control procedure by scanning electron microscopy. Sci.Rep, <span>14<span>, 17936 (2024), https://doi.org/10.1038/s41598-024-68797-7.</span></span></p> <p>It contains SEM and AFM-in-SEM images of polystyrene (PS) core particles, polystyrene-iron oxide (PS/Fe3O4) core-shell particles, and polystyrene-iron oxide-silica (PS/Fe3O4/SiO2) core-shell-shell particles. Please refer to the publication and its supporting information for more details on the acquisition and contents of the dataset, as well as the GitHub repository at https://github.Com/denizhulagu/roughness-analysis-by-electron-microscopy.</p> <p> </p> <p>The investigated particles were produced at BAM laboratories as previously described in:</p> <p>Hülagü, D. et al. Generalized analysis approach of the profile roughness by electron microscopy with the example of hierarchically grown polystyrene–iron oxide–silica core–shell–shell particles. Adv. Eng. Mater. 24, 2101344, https://doi.org/10.1002/adem.202101344 (2022).</p> <p>Tobias, C., Climent, E., Gawlitza, K. & Rurack, K. Polystyrene microparticles with convergently grown mesoporous silica shells as a promising tool for multiplexed bioanalytical assays. ACS Appl. Mater. Interfaces 13, 207, https://dx.doi.org/10.1021/acsami.0c17940 (2020).</p>
SmartRoadSense: aggregated road surface roughness dataset
<p>Dataset of aggregated road surface quality data points, collected through the mobile crowdsensing application SmartRoadSense.</p>
The Effect of Bristle Brush, Rubber Cup, and Air Polishing on Tooth Surface Roughness of Scaled Teeth
ClinicalTrials.gov study NCT06857474. IPD Sharing: YES. Countries: 1. Publications: 5.
Dynamic snow surface aerodynamic roughness lengths (z0) characterized by snow depths using LIDAR
Open the record for dataset details and reuse information.
Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
Open the record for dataset details and reuse information.
Data from: Spectral wear modelling of rubber friction on a hard substrate with large surface roughness
Open the record for dataset details and reuse information.
Experimental data for 'The transfer of forces through rough surface contact in concrete'
<p>The data contained in this archive was produced within the research project described in the following thesis:<br> <em>Tirassa M. (2020). The transfer of forces through rough surface contact in concrete. Ph.D. Thesis, EPFL, Lausanne, Switzerland.</em><br> The research project was funded by the Swiss National Science Foundation through research grant 200021_169649.<br> The data relates to the topic of force transfer across concrete cracks and interfaces (rebar-to-concrete) subjected to mixed mode kinematics with constant opening angle. It comprises the measured forces (normal and tangential) and displacements (crack opening and sliding). Moreover, some of the resulting surfaces (scanned using a digital microscope) are included.</p>
Scott-E surface roughness and thermal model results
<p>Supporting Information for "Geomorphic evidence for the presence of ice deposits in lunar permanently shadowed regions" by Moon et al. </p> <p><strong>Dataset S1 – S4.</strong></p> <p>Dataset S1. ds01_ElevRough.asc is an ASCII raster file for elevation-derived roughness [unitless].<br> Dataset S2. ds02_BriRough.asc is an ASCII raster file for brightness-derived roughness [DN/m].<br> Dataset S3. ds03_Tmax.asc is an ASCII raster file for modeled annual maximum temperature [K].<br> Dataset S4. ds04_Dice.asc is an ASCII raster file for modeled depth to thermally stable water ice [m].</p> <p>All maps are in south polar stereographic projection.</p>
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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.