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44 results for “surface flow”
SBC LTER: Ocean: HFR-derived surface flow metrics, surface water retention times, and related factors in the Santa Barbara Channel (2012-2019)
This data package include three files: 1. daily maps of High-Frequency Radar (HFR) measured surface currents, indices of mesoscale eddy locations, and local retention times on a 2km grid; 2. monthly time series of wind stress, alongshore pressure gradient, surface current EOF principal components, vorticity, eddy area, eddy presence, and spatially averaged retention times from January 2012 to December 2019; 3. A MATLAB script for plotting the maps and timeseries. These data were processed in order to investigate the drivers of surface water retention in the Santa Barbara Channel, CA, details of which are available in the study: Brokaw, R.J., D.A. Siegel, and L. Washburn. Physical Drivers of Surface Water Retention in the Santa Barbara Channel. [In preparation for Journal of Geophysical Research: Oceans.]
Satellite-observed surface flow speed within Russell sector, West Greenland, bi-weekly average of 2015-2019
<p>An average horizontal surface ice velocity of Russell sector (Greenland) with 2-week temporal and 150m spatial resolution. Derived from satellite images collected between 2015 and 2019 by Landsat-8, Sentinel-1, and Sentinel-2. The details on the data processing can be found in https://doi.org/10.5194/tc-2021-170.</p> <p><br> Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with maps of vx and vy velocity components, maps of associated uncertainties per velocity component (STD of the 2-weeks averaged raw satellite measurements), and map of number of averaged measurements.</p>
Seasonal evolution of basal conditions within Russell sector, West Greenland, inverted from satellite observations of surface flow
<p>An annual set of model-inferred basal and surface properties of ice flow at Russell Gletcher sector in Western Greenland with half-month temporal resolution. Derived using the Elmer/Ice ice-flow model by inversion of satellite-observed ice surface velocity (10.5281/zenodo.5535532). The details on the data creatoin can be found in 10.5194/tc-15-5675-2021 .</p> <p>Dataset contains 24 independent NetCDF files (one per 2-weeks time step) with:<br> * alpha - inverted be model basal friction coefficient in log10 (log10(MPa m-1 a)<br> * base - basal topography altitude (m)<br> * lithk - ice thickness (m)<br> * orog - surface altitude (m)<br> * strbasemag - magnitude of basal friction tb (MPa)<br> * xvelbase, yvelbase, zvelbase - 3D basal velocity (m/yr)<br> * xvelmean, yvelmean - vertically average mean horizontal velocity (m/yr)<br> * xvelsurf, yvelsurf, zvelsurf - 3D surface velocity (m/yr)<br> * n - effective pressure (MPa)</p> <p>The additional WinterMeanState NetCDF file (inversion from the mean velocity of january, Febriary, Mars) contains the same set of variables (except the effective pressure), and in addition contains the <em>As</em> Weertman sliding coeffitient.</p> <p>The results have been interpolated from the native unstructured model grid to the regular grid used for the observed velocity (10.5281/zenodo.5535624).</p>
WINTERC-G: a global upper mantle thermochemical model from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data
<p>WINTERC-G: A global, temperature and compositional model of the lithosphere<br> and upper mantle.<br> Version: v5.4, December 2020, J. Fullea, S. Lebedev, Z. Martinec, N. Celli<br> <br> Contact: Javier Fullea (jfullea@ucm.es)<br> Facultad de Fisica,<br> Universidad Complutense de Madrid (UCM),<br> Spain<br> ////////<br> Geophysics Section,<br> Dublin Institute for Advanced Studies<br> Dublin, Ireland<br> </p> <p>TYPE:<br> This contains files with:<br> i) the model directly on the triangular grid solved for in the surface wave inversion.</p> <p> ii) an interpolated grid at 0.5 deg lateral resolution for the density and density discontinuities used in the gravity field data inversion<br> </p> <p>If you have any questions regarding the methodology or the construction<br> of the model, please contact the authors. If you use the model, we would<br> request that you cite the reference indicated below, and appreciate<br> your feedback regarding the model and its application.</p> <p>Citation:</p> <p>Fullea, J., Lebedev, S., Martinec, Z., & Celli, N. L. (2021). WINTERC-G: mapping the upper mantle thermochemical heterogeneity from coupled geophysical–petrological inversion of seismic waveforms, heat flow, surface elevation and gravity satellite data. Geophysical Journal International, 226(1), 146-191.</p> <p>*******************************<br> Summary: construction of the model.<br> WINTERC-G is a Waveform tomography and Gravity (geoid and gravity anomalies and gradiometric measurements<br> from ESA's GOCE mission) INversion model of the TEmpeRature and Composition of the lithosphere and upper mantle at<br> global scale. WINTERC-G is based on upon the integrated geophysical-petrological<br> approach LitMod (Afonso et al., 2008; Fullea et al. 2009) and, hence, all<br> relevant mantle rock physical properties modelled (seismic velocities and density) are<br> computed within a thermodynamically self-consistent framework allowing for a direct<br> parameterization in terms of the temperature and composition of the lithosphere-upper<br> mantle. The inversion is a two-step procedure. In a first step, we invert surface-wave, Rayleigh and Love<br> fundamental mode dispersion curves from a high resolution global dataset measured using waveform inversion,<br> along with surface heat flow and elevation (isostasy) for temperature and crustal structure<br> using a point-wise, non-linear, gradient-search inversion<br> over a triangular grid with an average 225 km lateral inter-knot spacing. In a second step we<br> use a fully parallelized spherical harmonic formalism to invert satellite gravity field data in<br> order to refine the initial crustal density and mantle composition distributions from the step 1<br> for a fixed temperature field.</p> <p>The parameter space in step 1 includes crust (densities and S-wave velocities for a three-layered crust)<br> and mantle variables (the depth of the thermal Lithosphere-Athenosphere-Boundary,<br> the thickness of the sublithospheric thermal buffer, the sublithospheric temperatures at 3 different<br> equispaced nodes down to 400 km, the lithospheric and sublithospheric mantle compositon, and<br> the the radial anisotropy at the 3 crustal layers and at 56, 80, 110, 150, 200, 260, 330,<br> and 400 km depths.</p> <p>The parameter space in step 2 is defined by the average crustal density, and the<br> mantle composition in the lithosphere and sublithosphere.<br> We use the output crustal density from step 1 as the<br> initial value in step 2 inversion. Mantle densities are derived based on the output temperature<br> field from step 1 (kept fixed) and the bulk mantle composition inversion variables.</p> <p> </p> <p> </p> <p>*******************************</p> <p>This archive contains the following files:<br> README (this file)<br> WINTERC-G_Vp-Vs.lis (triangular grid)<br> WINTERC-G_rad_anis_Vs.lis (triangular grid)<br> WINTERC-G_Temperature.lis (triangular grid)<br> WINTERC-G_Density.lis (triangular grid)<br> WINTERC-G_LAB.lis (triangular grid)<br> WINTERC_T_rho_1D.z (1D average model of temperature and density)<br> rho_*_out.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_continental.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Ice.xyz (0.5 deg egular grid for gravity field)<br> ETOPO2_km_depth_Bed.xyz (0.5 deg egular grid for gravity field)<br> Global_Moho_WINTERC-G.xyz (0.5 deg egular grid for gravity field)</p> <p><br> Files in the triangular grid with an average 225 km lateral inter-knot spacing (12232 grid points):</p> <p>* WINTERC-G_Vp-Vs.lis: Vp and Vs (in km/s) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) Vp (km/s) Vs(km/s)<br> 5640 93.72 4.135 -5.0 3.91 2.11</p> <p><br> * WINTERC-G_rad_anis_Vs.lis: radial anisotropy, (Vsh-Vsv)/Vs_iso (in %) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) anisotropy (%)</p> <p>* WINTERC-G_Temperature.lis: temperature (in ºC) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth (km, <0 downwards) T (ºC) dT (%) dT(K) <br> 6437 297.20 -2.524 -259.000 1431.9 -1.91 -27.9<br> The anomalies dT are in % and K with respect to the 1D model in WINTERC_T_rho_1D.z (column 2).</p> <p>* WINTERC-G_Density.lis: density (in kg/m3) in all model columns with a vertical grid step of 2 km<br> Format for each column:<br> #Column number longitude latitude depth(km, <0 downwards) rho (kg/m3) drho(%) drho(kg/m3)<br> The anomalies drho are in % and kg/m3 with respect to the 1D model in WINTERC_T_rho_1D.z (column 3).</p> <p>* WINTERC_T_rho_1D.z: 1D average model of temperature (column 2 in ºC) and density (column 3 in kg/m3) with a vertical grid step of 2 km <br> 5.00000000 0.0000000000000000 6.0259973839110526<br> 3.00000000 0.0000000000000000 38.960571309690394<br> 1.00000000 0.33634006819423840 174.42296045978722<br> -1.00000000 3.8888495253719624 1692.8437489147236<br> -3.00000000 23.974111923225379 1863.8834351235944<br> -5.00000000 47.727920701943034 2568.2414495590924<br> -7.00000000 89.633398074381162 2819.8386016341910<br> -9.00000000 137.01489361657013 2839.5325893195904<br> -11.0000000 182.35233447017222 2897.6600872935287<br> -13.0000000 224.46247069572485 2945.2036923862997<br> -15.0000000 260.63395547331390 3069.6809340323475<br> -17.0000000 292.28175449521456 3132.4574175461721<br> -19.0000000 322.29571965406632 3145.5747337463940<br> -21.0000000 351.58698283375054 3157.2401512748038<br> -23.0000000 380.30002225705056 3177.0000420059773<br> -25.0000000 408.50259805632055 3183.6651032398490<br> -27.0000000 436.22632217636487 3190.9586785996116<br> -29.0000000 463.48733903170023 3198.9369509456310<br> -31.0000000 490.29841705549831 3209.7229872383764<br> -33.0000000 516.71149258457456 3221.6329506091679<br> ...</p> <p>Files in the interpolated regular grid at 0.5 deg lateral resolution used for gravity field data inversion:</p> <p> * rho_c_out.xyz: average crustal density<br> * rho_submoho_out.xyz: mantle density below the Moho discontinuity<br> * rho_*_out.xyz: mantle density defined at different model depths: 20, 35, 56, 80, 110, 150, 200, 260, 330 and 400 km.</p> <p> Format for the density files:<br> # longitude latitude density (kg/m3)<br> <br> Files containing layer discontinuities:</p> <p> * ETOPO2_km_continental.xyz: surface elevation including ice sheet and 0 in marine areas (km, <0 upwards)</p> <p> * ETOPO2_km_depth_Ice.xyz: surface elevation including ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * ETOPO2_km_depth_Bed.xyz: bedrock surface elevation without ice sheet (km, >0 downwards, <0 above sea level)</p> <p> * Global_Moho_WINTERC-G.xyz: crust-mantle discontinuity depth (km, >0 downwards)</p> <p> Format for the discontinuity files:<br> # longitude latitude depth (km)<br> <br> <br> The gravity field in WINTERC-G is computed using an spherical harmonic formalism and a model discretization<br> in 13 layers with laterally varying density. The first 7 layers are characterized by top and bottom boundaries with laterally varying radius whereas the last 6 layers are defined by top and bottom boundaries with constant radius:</p> <p>1/ Water: from ETOPO2_km_continental.xyz to ETOPO2_km_depth_Ice.xyz with rho=1030 kg/m3 (constant vertically)</p> <p>2/ Ice: from ETOPO2_km_depth_Ice.xyz to ETOPO2_km_depth_Bed.xyz with rho=910 kg/m3 (constant vertically)</p> <p>3/ Crust: from ETOPO2_km_depth_Bed to Global_Moho_WINTERC-G.xyz with rho=rho_c_out.xyz (constant vertically)</p> <p>4/ submoho-20km: from Global_Moho_WINTERC-G.xyz to z_20km (file with 20 km everywhere except where z_moho>20km) with rho=rho_submoho_out.xyz (top) and rho=rho_20km_out.xyz (bottom)</p> <p>5/ 20km-36km: from z_20km (file with 20 km everywhere except where z_moho>20km) to z_36km (file with 36 km everywhere except where z_moho>36km) with rho=rho_20km_out.xyz (top) and rho=rho_36km_out.xyz (bottom)</p> <p>6/ 36km-56km: from z_36km (file with 36 km everywhere except where z_moho>36km) to z_56km (file with 56 km everywhere except where z_moho>56km) with rho=rho_36km_out.xyz (top) and rho=rho_56km_out.xyz (bottom)</p> <p>7/ 56km-80km: from z_56km (file with 56 km everywhere except where z_moho>56km) to 80 km depth with rho=rho_56km_out.xyz (top) and rho=rho_80km_out.xyz (bottom)</p> <p>The next 6 layers are computed using the constant radius option:</p> <p>8/ 80km-110km: from z=80km to z=110 km with rho=rho_80km_out.xyz (top) and rho=rho_110km_out.xyz (bottom)</p> <p>9/ 110km-150km: from z=110km to z=150 km with rho=rho_110km_out.xyz (top) and rho=rho_150km_out.xyz (bottom)</p> <p>10/ 150km-200km: from z=150km to z=200 km with rho=rho_150km_out.xyz (top) and rho=rho_200km_out.xyz (bottom)</p> <p>11/ 200km-260km: from z=200km to z=260 km with rho=rho_200km_out.xyz (top) and rho=rho_260km_out.xyz (bottom)</p> <p>12/ 260km-330km: from z=260km to z=330 km with rho=rho_260km_out.xyz (top) and rho=rho_330km_out.xyz (bottom)</p> <p>13/ 330km-400km: from z=330km to z=400 km with rho=rho_330km_out.xyz (top) and rho=rho_400km_out.xyz (bottom)</p> <p> </p> <p> </p>
A new in vitro blood flow model for the realistic evaluation of antimicrobial surfaces
<p>Dataset to the publication</p> <p>A new <em>in vitro</em> blood flow model for the realistic evaluation of antimicrobial surfaces</p> <p>Juliane Valtin, Stephan Behrens, André Ruland, Florian Schmieder, Frank Sonntag, Lars D. Renner, Manfred F. Maitz, Carsten Werner</p> <p><em>Adv. Healthcare Mater.</em> 2023, 2301300. <a href="https://doi.org/10.1002/adhm.202301300">https://doi.org/10.1002/adhm.202301300</a></p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
Data on spatial distribution of tracers for optical sensing of stream surface flow
<p>Here, we present the numerical and field data used in the manuscript entitled <em>Spatial distribution of tracers for optical sensing of stream surface flow</em>. Numerical data were synthetically generated considering different values of seeding density and aggregation levels of tracers for image-velocimetry analyses. In total, 33,600 synthetic images were generated. Field data correspond with the Basento River case study located in southern Italy. The respective footage at 12 fps, pre-processed and stabilised frames, and reference velocity data are provided in this dataset.</p>
Laboratory Open Channel Flow: Video, Waterlevel and Surface Velocity
<p>Video footage of an open channel flow in a laboratory setting, associated with the surface velocity and water level.</p> <p><br> - Video footage was collected using a Raspberry Pi Camera Module v2 attached to a Raspberry Pi 4 at 25fps from three positions and split into roughly 15s chunks.<br> - A "mic+35/IU/TC" ultrasonic sensor (accuracy: ±1%) measured the water level<br> - A "Nortek Vectrino" (accuracy: ±1% ±1mm/s) velocimeter measured the velocity at the surface</p> <p> </p> <p>- The video files can be found in the folders position1, position2 and position3, each file name contains the initial timestamp to match frames to the measurements<br> - The file "waterlevel.csv" contains the timestamps, Waterlevel [mm] and Percentage Full [%]. The waterlevel column is reversed, as the distance between the sensor and the surface was measured. This means, that lower values correspond to higher water levels.<br> - The file "velocity.csv" contains the timestamps and surface velocity measurements [m/s]</p>
The thermal state of Volgo–Uralia from Bayesian inversion of surface heat flow and temperature [data set]
<p>This collection contains the dataset and the code which were used to find the thermal parameters’ lateral variations of the Volgo–Uralian subcraton through the Bayesian Markov Chain Monte Carlo (MCMC) statistical approach. The code originally was given in the analogous study of Antarctica's geothermal structure by Lösing et al. (2020) and it can be found in https://github.com/MareenLoesing/GHF-Antarctica-Bayesian. The main changes to the code of Lösing et al. (2020) are listed in the section 2 of the readme file.</p> <p>For an official use of the Bayesian inversion code please also cite: Lösing, M., Ebbing, J. & Szwillus, W. (2020) Geothermal Heat Flux in Antarctica: Assessing Models and Observations by Bayesian Inversion. Front. Earth Sci., 8, 105. doi:10.3389/feart.2020.00105</p> <p>The lateral variations of the thermal parameters for the single-layer and multi-layer crust are saved in “GHF_Volgo-Uralia_Single-layer.csv” and “GHF_Volgo-Uralia_Multi-layer.csv” respectively.</p>
High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory
<p>The identification of the air-water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air-water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.</p>
Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows
<p>Data used for the preparation of the manuscript "Drifter deployment strategies to determine Lagrangian surface convergence in submesoscale flows".</p>
Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows
<p>Title: <strong>Safe and Just Earth Systems Boundaries for Surface Water: Hydrologic Alteration of Environmental Flows</strong> Author: Pamela A. Green (<a href="mailto:pg@pamelaagreen.com">pg@pamelaagreen.com</a>), Advanced Science Research Center, CUNY, New York, NY USA <a href="https://orcid.org/0009-0006-7803-8182">https://orcid.org/0009-0006-7803-8182</a></p> <p>The python Jupyter Notebook <strong>SafeJustEarthSysBnd_EstressCUNY-Griffith2022-23.ipynb</strong> and accompanying data sets represent spatial modelling for development of the safe and just surface water target for Working Group 3 of the Earth Commission for the Earth Commission Long Report and the "Safe and Just Earth Systems Boundaries" publication. The surface water target includes spatial modelling of the extent of global-scale hydrological alteration of environmental flows.</p> <p>All input datasets required to run the model are located under the <strong>ModelInput</strong> folder with raster data in zipped format to minimize space requirements. The code extracts the zipped files and then deletes the uncompressed files upon completion. All model outputs are located under the <strong>ModelOutput</strong> folder.</p> <p>Please reference the <strong>README.xlsx</strong> file for a full listing of the model input and output data files.</p>
Surface flow velocity from Pulmanki, Koita and Sävar Rivers 2020-2022
<p>Data description:<br>Surface flow velocity dataset was created using Hydro-STIV software (Hydro-STIV v.1.2.2, Hydro Technology Institute co.), which uses Space-Time Image Velocimetry (STIV) for velocity estimation, a method derived from Large-Scale Particle Image Velocimetry (LSPIV) developed by Fujita in 2007 (Fujita et al., 2007). The videos were georeferenced using known GCPs and the software performed orthorectification and calibration. The data has been used for publication "Surface flow and ice rafting velocities during freezing and thawing periods in Nordic rivers". Data consists of videos and daily stil images from Pulmanki, Koita and Sävar Rivers from Autumn freezing and Spring thawing periods. The data from Koita River is from 2020-2021 and from Pulmanki and Sävar Rivers from 2021-2022. The original raw data based on which the STIV analysis was performed was collected with Burrel time-lapse RGB cameras. </p> <p> </p> <p>Acknowledgements:<br><span>The river-ice related measurements were initiated at Pulmankijoki River in 2014 under the post-doctoral research project of Dr Lotsari, funded by the Research Council of Finland (ExRIVER: grant number 267345), and this study is a continuum in the series of these winter season studies. The work for this study was financially supported by four other projects funded by the Research Council of Finland (DefrostingRivers: 338480; HYDRO-RDI-Network: 337394; Digital Waters [DIWA] Flagship;359248). In addition, the work was funded by The European Union – NextGenerationEU Recovery instrument (RRF) through Research Council of Finland projects Hydro RI Platform (346167) and Green-Digi-Basin (347703). The Department of Geographical and Historical Studies, University of Eastern Finland, supported financially the field work done at Koita River. The work by Dr Lina Polvi-Sjöberg at the Sävar River was financed by a grant (2023-01513) from the Swedish Research Council Formas.</span></p>
Data and scripts for Journal of Geophysical Research – Earth Surface publication: Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina
<p>This data source contains scripts and data associated with the JGR Earth Surface publication <strong>“Identification of debris-flow channels using high-resolution topographic data: A case study in the Quebrada del Toro, NW Argentina”</strong> by A. Mueting, B. Bookhagen, and M. R. Strecker. The Digital Elevation Model (DEM) of the lower part of the Quebrada del Toro and Río Capilla catchment in the NW Argentinian Andes was generated from SPOT-7 tri-stereo images using Ames Stereo Pipeline. The final dataset has a spatial resolution of 3 m. A full description of the DEM generation process and accuracy assessment can be found in the associated paper. The scripts are also available at https://github.com/UP-RS-ESP/DEM_ConnectedComponents.</p>
Datasets of surface ice flow speed for Pine Island Glacier, Ferrigno Ice Stream and Leonardo Glacier
<p>Here we make available the datasets used in my MRes dissertation as part of the School of Earth and Environment at the University of Leeds. The research paper presents a novel method to automatically detect speed anomalies in ice velocity datasets.</p>
Fluid flow drives phenotypic heterogeneity in bacterial growth and adhesion on surfaces
<p>Raw data for the study reported by Hubert et al., Nature Communications 2024 (accepted).</p> <p>Each of the 4 folders within the archive, denoted 'Ulow', 'Low', 'Med', and 'High' contains 3 sets of raw experimental data, each corresponding to an experiment performed in the 'Ulow', 'Low', 'Med', and 'High' shear stress regime, in accordance with the folders' names.</p> <p>Each of these data sets is bundled into a single .avi video, consisting of raw images recorded at a 1/60 Hz acquisition frequency (i.e., 1 image per minute).</p> <p>The folder 'Supplementary_Software'contains Matlab and Python scripts used to treat the raw data, with a raw data sample. The file 'HOWTO_use_the_scripts.txt' explains how to use the scripts.</p> <p>Except for this HOWTO file, the files inside the various subfolders of folder 'Supplementary_Software' are not listed in the file tree below. Only the subfolders are listed.</p> <p>./<br>├── README_data_sets_and_treatment_scripts.txt (this file)<br>├── Ulow/<br> | ├── Ulow_set1.avi<br> | ├── Ulow_set2.avi<br> | └── Ulow_set3.avi<br>├── Low/<br> | ├── Low_set1.avi<br> | ├── Low_set2.avi<br> | └── Low_set3.avi<br>├── Med/<br> | ├── Med_set1.avi<br> | ├── Med_set2.avi<br> | └── Med_set3.avi<br>└── High/<br> | ├── High_set1.avi<br> | ├── High_set2.avi<br> | └── High_set3.avi<br>└── Supplementary_Software/<br> ├── HOWTO_use_the_scripts.txt<br> ├── step1_matlab/ <br> | ├── Pos0_100_every_1000/ <br> | ├── matlab_image_processing/ <br> | ├── images_treated_20201120_220854/ <br> | └── Outputs/<br> └── step2_python/<br> └── Outputs/</p> <p> </p> <p> </p>
Magneto-Stokes Flow in a Shallow Free-Surface Annulus
<p>In this study, we analyse "magneto-Stokes" flow, a fundamental magnetohydrodynamic (MHD) flow that shares the cylindrical-annular geometry of the Taylor-Couette cell, but uses applied electromagnetic forces to circulate a free-surface layer of electrolyte at low Reynolds numbers. The first complete, analytical solution for time-dependent magneto-Stokes flow is presented and validated with coupled laboratory and numerical experiments. Three regimes are distinguished (shallow-layer, transitional, and deep-layer flow regimes), and their influence on the efficiency of microscale mixing is clarified. The solution in the shallow-layer limit belongs to a newly-identified class of MHD potential flows, and thus induces mixing without the aid of axial vorticity. We show that these shallow-layer magneto-Stokes flows can still augment mixing in distinct Taylor dispersion and advection-dominated mixing regimes. The existence of enhanced mixing across all three distinguished flow regimes is predicted by asymptotic scaling laws and supported by three-dimensional numerical simulations. Mixing enhancement is initiated with the least electromagnetic forcing in channels with order-unity depth-to-gap-width ratios. If the strength of the electromagnetic forcing is not a constraint, then shallow-layer flows can still yield the shortest mixing times in the advection-dominated limit. Our robust description of momentum evolution and mixing of passive tracers makes the annular magneto-Stokes system fit for use as an MHD reference flow.</p>
Implications of Lateral Groundwater Flow Across Varying Spatial Resolutions in Global Land Surface Modeling
<p>This folder contains the data used for plotting and analysis in the manuscript.</p>
Movies and temperature and pressure measurements associated with the study "Experimental evidence for lava-like mud flows under Martian surface conditions"
<p>Movies and temperature and pressure data associated with the study "<strong>Experimental evidence for lava-like mud flows under Martian surface conditions</strong>".</p>
Flow Cytometry Data from "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing"
<p>This record contains the flow cytometry data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing."</p> <p>Files are in <a href="https://docs.flowjo.com/flowjo/advanced-features/fj-acs/">Archive Cytometry Standard (ACS) format</a> . Each <code>.acs</code> file is a zip container which holds both the raw <code>.fcs</code> files and a FlowJo workspace (<code>.wsp</code>) file.</p> <p>Keywords in the workspace file identify which primary antibody (<code>primary</code>) was used and which cell genotype (<code>strain</code>) was used for each sample. The workspace also encodes the gating scheme and compensation matrix applied to each sample. Plots in the manuscript are exported from Layout views in the workspace.</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.