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391 results for “Roughness”
Wind power plants layouts according to arbitrary reference points, Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array
<p><strong>If this dataset helps your research, please cite it and the papers further below (according to which OWPP you study).</strong></p> <p>This dataset contains the layouts of the Thanet, West of Duddon Sands, Ormonde, Westermost Rough, Horns Rev 1 & 2, Anholt, and London Array offshore wind power plants (OWPPs), which can be utilized in a variety of studies. </p> <p>The X and Y coordinates, in kilometers, were written according to arbitrary reference points. The positions of wind turbine generators (WTGs) and substations (SS) for the OWPPs came from the sources below.</p> <p><strong>Thanet</strong>: WTGs from [1] (page 7), SS based on [3] (page 9).<br><strong>West of Duddon Sands</strong>: WTGs from [2] (page 5), SS based on [3] (page 9).<br><strong>Ormonde</strong>: WTGs and SS from [4] (page 2).<br><strong>Westermost Rough</strong>: WTGs and SS from [5] (page 4).<br><strong>Horns Rev 1</strong>: WTGs and SS from [6] (page 6).<br><strong>Horns Rev 2</strong>: WTGs and SS from [7] (page 5).<br><strong>Anholt</strong>: WTGs and SS from [8] (page 3).<br><strong>London Array</strong>: WTGs from [9] (page 15), SS based on [10] (page 2).</p> <p>From the OWPPs' layout figures [1-9], I used Graph Grabber 2.0.2* to extract the data points. Then, based on visual inspection of layouts in [1-9], I utilized 2D projections to align WTGs that should be aligned. References [1], [2], and [9] do not provide the SS positions. Thus, I carefully overlapped the layouts with other layouts from [3] and [10] to approximate the SS locations.</p> <p>Regarding the arbitrary reference points for the coordinates, although the values in the X and Y axes differ from [1-9], note that the distances among the WTGs are the same from [1-9]. Furthermore, there are no axes in [1,5]. Instead, distances are given, which are enough to obtain the layout. Values in meters were converted to kilometers.</p> <p>As seen in the attachments, the coordinates can be obtained via either "h5" or "csv" files, which can be easily read by Matlab, Julia, and Python, among others. <strong>The name of the datasets in the "h5" file are</strong>: "Thanet", "WDS", "Ormonde", "WMR", "HornsRev1", "HornsRev2", "Anholt", and "LondonArray".</p> <p><strong>Except for the London Array OWPP, the first row in all data matrices represents the SS coordinates, whereas the subsequent rows represent the WTGs. London Array has 2 substations, thus the first and second rows represent their coordinates. In all matrices, the first and second columns are the X and Y coordinates, respectively.</strong></p> <p>*<a href="https://www.quintessa.org/software/downloads-and-demos/graph-grabber-2.0.2">Graph Grabber 2.0.2 | Downloads And Demos | Software | Quintessa Limited | Scientific and Mathematical Consultancy</a></p>
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.
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
<p>This file contains the ADV data of <em>Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project</em>.</p>
Datasets for Wohlfarth et al. (2023) An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer
<p>This document describes the datasets and modeling results presented and discussed in our full research article.<br> <br> Wohlfarth, K., Wöhler, C., Hiesinger, H., Helbert, J. 2023, An advanced thermal roughness model for airless planetary bodies - Implications for global variations of lunar hydration and mineralogical mapping of Mercury with the MERTIS spectrometer, Astronomy and Astrophysics, 672<br> <br> <a href="https://doi.org/10.1051/0004-6361/202245343">https://doi.org/10.1051/0004-6361/202245343</a><br> <br> We provide several visualization scripts that read and display the results for convenience. Access to the original MATLAB® code for the thermal model implementation is available upon request (<a href="mailto:kay.wohlfarth@tu-dortmund.de">kay.wohlfarth@tu-dortmund.de</a>).<br> <br> More info in Dataproducts.pdf</p>
Interaction of waves with idealized high-relief bottom roughness, model parameters and code
Interactions between waves and large bottom roughness were investigated using Large Eddy Simulations of oscillatory flow over infinite hemisphere arrays. Simulations were made using the open source computational fluid dynamics code OpenFOAM. Wave amplitude, period, and hemisphere spacing were varied to investigate the dependence of kinematics and dynamics on dimensionless parameters, including the Keulegan-Carpenter number (KC), the ratio of wave orbital excursion to roughness element size. This archive includes the input files and user written routines to reproduce the simulations in the paper. Yu, X., J. H. Rosman, and J. L. Hench, 2018. Interaction of waves with idealized high-relief bottom roughness, Journal of Geophysical Research (Oceans), doi:10.1002/2017JC013515.
PIE LTER measurements of the two tallest Phragmites australis stems per five meter interval along transects at the Argilla Rd. salt marsh restoration site (Ipswich, MA) and Rough Meadows reference marsh (Rowley, MA – Stackyard Road area).
The file contains measurements of the two tallest Phragmites australis stems per five meter interval along transects at the Argilla Rd. salt marsh restoration site (Ipswich, MA) and Rough Meadows reference marsh (Rowley, MA – Stackyard Road area). The culvert feeding Argilla Marsh was enlarged in late November 1998, thus data from 1999 onward is considered as representing a hydrologically restored marsh. A long term study not directly part of the PIE LTER, but a companion study related to tidal restrictions and hydrological alterations of salt marshes in the Plum Island ecosystem.
PIE LTER plant species percent cover in quadrats along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area), Massachusetts.
Plant species percent cover in quadrats along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area), Massachusetts. A long term study not directly part of the PIE LTER, but a companion study related to tidal restrictions and hydrological alterations of salt marshes in the Plum Island ecosystem.
PIE LTER geographic information regarding vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich and a reference marsh (Rough Meadows) in Rowley, Massachusetts.
A description of the vegetation transects set up at the Argilla Rd. Salt marsh restoration site in Ipswich, MA and a reference marsh (Rough Meadows) in Rowley, MA.
PIE LTER plant species presence along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area), Massachusetts.
Plant species presence along vegetation transects at the Argilla Rd. salt marsh restoration site (Ipswich) and Rough Meadows reference marsh (Rowley – Stackyard Road area). A long term study not directly part of the PIE LTER, but a companion study related to tidal restrictions and hydrological alterations of salt marshes in the Plum Island ecosystem..
SAFE topographic roughness
<b>Description: </b><p>These GeoTIFFs contain two different measurements of topographic roughness for the SAFE landscape (Wilson <i>et al.</i> 2007, Riley <i>et al.</i> 1999) These have been calculated using the raster package in R from SRTM data processed for use at SAFE (<a href="https://www.zenodo.org/record/3490488">https://www.zenodo.org/record/3490488</a>).<br><br>Further details of the dataset and processing can be found at <a href="https://www.safeproject.net/dokuwiki/safe_gis/topographic_roughness">https://www.safeproject.net/dokuwiki/safe_gis/topographic_roughness</a>. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3697796">here</a></p><p><b>Files: </b>This dataset consists of 3 files: SAFE_topographic_roughness.xlsx, SRTM_UTM50N_TRI_Riley1999.tif, SRTM_UTM50N_TRI_Wilson2007.tif</p><p><b>SAFE_topographic_roughness.xlsx</b></p><p>This file only contains metadata for the files below</p><p><b>SRTM_UTM50N_TRI_Riley1999.tif</b></p><p>Description: Riley et al. 1999 Topographic Roughness Index for the SAFE landscape</p><p><b>SRTM_UTM50N_TRI_Wilson2007.tif</b></p><p>Description: Wilson et al. 2007 Topographic Roughness Index for the SAFE landscape</p><p><b>Date range: </b>2010-10-01 to 2020-03-05</p><p><b>Latitudinal extent: </b>4.0223 to 5.9761</p><p><b>Longitudinal extent: </b>116.0242 to 117.9758</p>
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>
Dataset of micro-roughness, Schmidt hammer and reflectance spectra obtained at Hallstaetter Glacier foreland
<p>The files contain data of micro-roughness (Ra and Rz), raw data of Schmidt hammer rebound-values, and reflectance spectra obtained at Hallstaetter Glacier foreland in July 2022. The data was use in a publication: Dąbski M, Badura I, Kycko M, Grabarczyk A, Matlakowska R, Otto J-C. The Development of Limestone Weathering Rind in a Proglacial Environment of the Hallstätter Glacier. <i>Minerals</i>. 2023; 13(4):530. https://doi.org/10.3390/min13040530. </p><p>Funding provided by National Science Centre, Poland (Preludium Bis-2 2020/39/O/ST10/01068).</p><p>Micro-roughness, rock strength (Schmidt hammer rebound values), and spectral reflectance were obtained in-situ on glacially abraded rock surfaces along a transect from the glacial snout to the outermost moraines from the Little Ice Age, covering circa 172 years of subaerial weathering in the proglacial alpine environment. UAV surveys of the studied area were performed to obtain Digital Elevation Models (DEMs) and allow for detailed comparative studies in the future.</p><p>Test site 1 was very close to the glacier (undergoes weathering for 1–2 years), site 2 was in the zone c. 10 years old, site 3 was in the zone c. 50–51 years old, site 4 was in the zone c. 105–106 years old, and the last one (site 5) was on the LIA moraines, where the duration of weathering is c. 167–172 years. The sites were located on bedrock or boulders embedded in the moraines with distinct traces of glacial abrasion, allowing us to infer that older weathering rind (developed before glacial accumulation) has been eroded. The sites were selected based on their age, homogenous petrography, accessibility, and suitability for micro-roughness measurements. Within each test site, we selected ten specific rock surfaces (c. 100 cm2 each), with clear signs of glacial abrasion, for the measurements of micro-roughness, Schmidt hammer rebound (rock strength), and spectral reflectance. </p><p> </p><p> </p>
Disentangling developmental effects of play aspects in rat rough-and-tumble play
<p>Animal play encompasses a variety of aspects, with kinematic and social aspects being particularly prevalent in mammalian play behaviour. While the developmental effects of play have been increasingly documented in recent decades, understanding the specific contributions of different play aspects remains crucial to understand the function and evolutionary benefit of animal play. In our study, developing male rats were exposed to rough-and-tumble (RT) play selectively reduced in either the kinematic or the social aspect. We then assessed the developmental effects of reduced play on their appraisal of standardised human-rat play ('tickling') by examining their emission of 50-kHz ultrasonic vocalisations (USVs). Using a deep learning framework, we efficiently classified five subtypes of these USV across six behaviour states. Our results revealed that rats lacking the kinematic aspect in play emitted fewer USVs during tactile contacts by human and generally produced fewer USVs of positive valence compared to control rats. Rats lacking the social aspect did not differ from the control and the kinematically reduced group. These results indicate aspects of play have different developmental effects, underscoring the need for researchers to further disentangle how each aspect affects animals.</p>
Dataset of micro-roughness, Schmidt hammer and reflectance spectra obtained at Midtre Lovénbreen foreland
<p>The files contain data of micro-roughness (Ra and Rz), raw and corrected data of Schmidt hammer rebound-values, and reflectance spectra obtained at Midtre Lovénbreen glacier foreland in July 2023. </p> <p>Funding provided by National Science Centre, Poland (Preludium Bis-2 2020/39/O/ST10/01068).</p> <p>Micro-roughness, rock strength (Schmidt hammer rebound values), and spectral reflectance were obtained in-situ on glacially abraded rock surfaces along a transect from the glacial snout to the outermost moraines from the Little Ice Age, covering circa 118 years of subaerial weathering in the proglacial polar environment. UAV surveys of the studied area were performed to obtain Digital Elevation Models (DEMs) and allow for detailed comparative studies in the future.</p> <p>Test site 1 was very close to the glacier (undergoes weathering for 3 years), site 2 was in the zone c. 43 years old, site 3 was in the zone c. 63 years old, site 4 was in the zone c. 87 years old, and the last one (site 5) was on the LIA moraines, where the duration of weathering is c. 118 years. The sites were located on biotite gneiss boulders embedded in the moraines with distinct traces of glacial abrasion, allowing us to infer that older weathering rind (developed before glacial accumulation) has been eroded. The sites were selected based on their age, homogenous petrography, accessibility, and suitability for micro-roughness measurements. Within each test site, we selected ten specific rock surfaces (c. 100 cm2 each), with clear signs of glacial abrasion, for the measurements of micro-roughness, Schmidt hammer rebound (rock strength), and spectral reflectance. </p> <p> </p>
Scream's roughness grants privileged access to the brain during sleep. - audio files
<p>Audio files played during the experiment described in:</p> <pre>https://doi.org/10.5281/zenodo.8407716</pre> <p>Audio files are identified as follows: N_cond_X_st_Y.wav.</p> <p>X corresponds to the condition, where 1 represents a scream and 2 represents a neutral vocalization.</p> <p>Y corresponds to the identifier of the actor who produced the vocalization.</p> <p> </p>
Dataset for "Coating thickness prediction for a viscous film on a rough plate"
<p>This dataset supports the publication 'Coating thickness prediction for a viscous film on a rough plate' by Lebo Molefe, Giuseppe A. Zampogna, John M. Kolinski, and François Gallaire, <em>Journal of Fluid Mechanics,</em> <strong>1001</strong>(A59), 2024. The data are film thicknesses measured for silicone oil films coated on rough plates. Please refer to the publication and its supplemental material for details.</p> <p><a href="https://doi.org/10.1017/jfm.2024.1015">https://doi.org/10.1017/jfm.2024.1015</a></p> <p><strong>Contents</strong></p> <p>Three folders containing the code and data required to produce the figures in the journal article and supplemental material.</p> <p><strong>Readme</strong></p> <p>A .txt file describing contents of code and datasets.</p> <p><strong>Code</strong></p> <p>Contains:</p> <ul> <li>Python code to produce Figures 5-10 and 12 in the main text, as well as supplementary Figures 1-4.</li> <li>Source code: COMSOL files for solving microscopic problem for effective parameters (L, Kitf) describing the rough surface; Python files for solving the macroscopic model equations once effective parameters are known.</li> </ul> <p><strong>Data</strong></p> <p>Contains data needed to plot the figures mentioned above, as well as supporting data.</p> <p>The data includes density, surface tension, and viscosity measurements for silicone oil, with rheometry measurements performed on an Anton Paar MCR 302 rheometer, as detailed in the Supplementary Material.</p> <p><strong>Plots</strong></p> <p>Contains output of plotting code corresponding to the figures mentioned above.</p>
Dataset: Effect of Macrorough Sidewalls on Flow Resistance in Steep Rough Channels
<p>The data set includes reach-averaged flow velocity measurements and bed and sidewall roughness parameters for flume experiments conducted at the Laboratory of Hydraulics, Hydrology, and Glaciology (VAW) at ETH Zurich.</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>
AI4AGRI Soil roughness estimation dataset
<p>The dataset contains two sets of images, representing digital images of soil samples used for training ML models to estimate the soil roughness. For more details on the experiments please refer to (and cite): Ivanovici, M., Popa, S., Marandskiy, K., & Florea, C. (2024). Deep automatic soil roughness estimation from digital images. European Journal of Remote Sensing. DOI: https://doi.org/10.1080/22797254.2024.2342955 </p> <p>The zip archives containts a readme text file with the detailed description of the dataset.</p> <p>Funded by the European Union. The AI4AGRI project entitled “Romanian Excellence Center on Artificial Intelligence on Earth Observation Data for Agriculture” received funding from the European Union’s Horizon Europe research and innovation programme under the grant agreement no. 101079136. </p> <p>Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. </p>
ScienceDex guides
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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.