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1,271 results for “Data Flow”
Shadowgraph measurements of rotating convective planetary core-style flows (data, code, and figures)
<p>Data files and accompanying matlab analysis code for the paper: "Shadowgraph Measurements of Rotating Convective Planetary Core-Style Flows"</p>
Rock-magnetic, paleomagnetic and paleointensity data from a lava flow erupted on 4 December 2021 in La Palma (Canary Islands, Spain)
<p><span>The folder “1 Rock magnetic data VFTB La Palma” contains data in .txt format of IRM acquisition curves (extension .irm), hysteresis curves (extension .hys), backfield curves (extension .coe) and thermomagnetic curves (extension .rmp) obtained on a lava flow erupted on December 4<sup>th</sup>, 2021 in La Palma (Canary Islands, Spain). Extension .rmp files including Ms-T in their file name are for magnetisation vs. temperature measurements and those including k-T in their file name are for susceptibility vs. temperature measurements. Thermomagnetic and hysteresis measurements were first performed on original specimens and then again on the same specimens after having been heated. Files including “antes” in their filename are the original measurements and those including “despues” in their file name correspond to heated samples. All measurements were performed on a Variable Field Translation Balance (VFTB) in the paleomagnetic laboratory of the University of Burgos (Spain). Columns are separated by tabs. Data can be visualised and analysed with the </span><span>RockMagAnalyzer 1.0 software (Leonhardt, 2006).</span></p> <p><span> </span></p> <p><span>The folder “2 Rock magnetic data k_fd and IRM analysis” contains four files of IRM acquisition data for the analysis of coercivity spectra (CV2-4ir0.asc, CV2-4ir7.asc, CV2-17i0.asc, CV2-17i7.asc) and one file with low- and high-frequency (470 and 4750 Hz) susceptibility data (susceptibility.asc) </span><span>obtained on a lava flow erupted on December 4<sup>th</sup>, 2021, in La Palma (Canary Islands, Spain). All files have an .asc extension containing characters in ASCII format.<a name="_Hlk171719051"></a> </span></p> <p><span><span>IRM acquisition data: (i) </span></span><span><span>File CV2-4ir0.asc</span></span><span>: progressive IRM acquisition of sample CV02-04 with remanence measurement immediately after field application. (ii) File CV2-4ir7.asc: progressive IRM acquisition of sample CV02-04 with remanence measurement 7 minutes after field application. (iii) File CV2-17i0.asc: progressive IRM acquisition of sample CV02-17 with remanence measurement immediately after field application. (iv) </span><span>File CV2-17i7.asc:</span><span> progressive IRM acquisition of sample CV02-17 with remanence measurement 7 minutes after field application. Column 1: measurement number; column DEMAG: first step<span> </span>(100mT) is zero value after AF demagnetisation at 100 mT, following values are IRM acquisition field steps in mT; columns CD, CI, ISD, ISI, RD, RI are declination and inclination values in sample, field corrected and bedding corrected coordinates; column M: magnetic moment in emu; column J: magnetisation in emu/g; columns X, Y, Z display magnetic moment X, Y and Z coordinates. As in IRM acquisition experiments the applied field was directed towards the sample z-axis, IRM values can be obtained by dividing column Z by the sample mass value (given in g), which is found under the tag “SIZE”.<span> </span>Coercivity spectra analysis has been performed with the MAX UnMix software (Maxbauer et al., 2016).</span></p> <p><span>File susceptibility.asc includes data from three </span><span>low-frequency (LF) and three high-frequency (HF) susceptibility measurements performed on two samples.</span></p> <p><span> </span></p> <p><span>The folder “3 Rock magnetic data FORC” contains three files with first order reversal curves data data obtained on three samples from a lava flow erupted on December 4<sup>th</sup>, 2021, in La Palma (Canary Islands, Spain). Data can be analysed using the </span><span>FORCinel software (Harrison and Feinberg, 2008).</span></p> <p><span> </span></p> <p><span>The folder “4 Paleomagnetic data La Palma” contains two folders with paleomagnetic thermal and alternating field demagnetisation data obtained on a lava flow erupted on December 4<sup>th</sup>, 2021, in La Palma (Canary Islands, Spain). Measurements were performed with a cryogenic magnetometer in the paleomagnetic laboratory of the University of Burgos (Spain). Data are in .txt format with the extension .rs3. Columns are separated by empty spaces. In AF measurements, a value of 100 must be subtracted from all AF demagnetisation steps to obtain the real AF-step value (i.e., a demagnetisation step of 165 really means 65 mT). Data can be visualised and analysed with the </span><span>Remasoft software (Chadima and Hrouda, 2006).</span></p> <p><span> </span></p> <p><span>The folder “</span><span>5 Thellier-Coe paleointensity data</span><span>” contains paleointensity determination data obtained with the Thellier-Coe method on a lava flow erupted on December 4<sup>th</sup>, 2021 in La Palma (Canary Islands, Spain). Experiments were carried out at the paleomagnetic laboratory of the University of Burgos (Spain). Data are in .txt format with the extension .tdt separated by tabs. Data can be visualised and analysed with the </span><span>ThellierTool software (Leonhardt et al.,2004).</span></p> <p><span> </span></p> <p><span>The folder “6 Multispecimen paleointen</span><span>sity data” contains a file (CVC02_MSP_Am2.txt) with </span><span>data of 3 paleointensity determinations from the Tajogaite volcano eruption on December 4th, 2021, in the island of La Palma (Canary Islands, Spain). These data were obtained with the multispecimen method (Biggin and Poidras, 2006; Dekkers and Böhnel, 2006; Fabian and Leonhardt, 2010) at the paleomagnetic laboratory of the University of Burgos (Spain). The data are in the "MSP generic format" for the online application Paleoinetnsity.org (Béguin et al., 2020), Multispecimen Protocol option. Each determination consists of five different heating steps (m0, m1, m2, m3 and m4) applied to 8 different specimens.</span></p> <p><span> </span></p> <p><span>The folder “7 Tsunakawa-Shaw paleointensity” contains four folders with paleointensity determination data obtained with the Tsunakawa-Shaw method. The folder named “csv” contains the outcome of the best fit interpretation performed by the Jupyter notebook. The folder “d” contains the original demagnetization data obtained for each specimen. The folder “MagIC” contains the outcome compatible with MagIC software. The folder “plots” contains the outcome plots of the best fit interpretation in .pdf format. These experiments were carried out in the <em>Paleomagnetism Laboratory at the Kochi Core Centre in Kochi University, Japan</em>. Data can be visualised and analysed through Jupyter, using the “TS_analysis_G-cubed_r20230714_cvc02” available in the folder. </span></p> <p><strong><span> </span></strong></p> <p><strong><span>REFERENCES</span></strong></p> <p><span> </span></p> <p><span>Béguin, A., Paterson, G. A., Biggin, A. J., & de Groot, L. V. (2020).<span> </span>Paleointensity org: an online, open source, application for the interpretation of paleointensity data. Geochemistry, Geophysics, Geosystems, 21, e2019GC008791,</span> <span>https://doi.org/10.1029/2019GC008791<span> </span>.</span></p> <p><span>Biggin, A., Poidras, T., 2006. First-order symmetry of weak-field partial thermoremanence in multi-domain ferromagnetic grains. 1. Experimental evidence and physical implications. Earth Planet. Sci. Lett. 245, 438–453. doi:10.1016/j.epsl.2006.02.035</span></p> <p><span>Chadima, M. and Hrouda, F., 2006. Remasoft 3.0 a user friendly paleomagnetic data browser and analyzer. <em>Travaux Géophysiques</em>, XXVII, 20-21.</span></p> <p><span>Dekkers, M.J., Böhnel, H.N., 2006. Reliable absolute palaeointensities independent of magnetic domain state. Earth Planet. Sci. Lett. 248, 507–516. doi:10.1016/j.epsl.2006.05.040</span></p> <p><span>Fabian, K., Leonhardt, R., 2010. Multiple-specimen absolute paleointensity determination: An optimal protocol including pTRM normalization, domain-state correction, and alteration test. Earth Planet. Sci. Lett. 297, 84–94. doi:10.1016/j.epsl.2010.06.006</span></p> <p><span>Harrison, R.J. and Feinberg, J.M. (2008), FORCinel: An improved algorithm for calculating first-order reversal curve distributions using locally weighted regression smoothing. <em>Geochem. Geophys. Geosyst.</em>, 9, Q05016, doi:10.1029/2008GC001987.</span></p> <p><span>Leonhardt, R., Heunemann, C. and Krása, D., 2004. Analyzing absolute paleointensity determinations: Acceptance criteria and the software ThellierTool4.0. <em>Geochem. Geophys. Geosyst.</em>, Vol. 5, no. 12, doi.: 10.1029/2004GC000807.</span></p> <p><span>Leonhardt, R., 2006. Analyzing rock magnetic measurements; The RockMagAnalyzer 1.0 software. <em>Computers and Geosciences</em>, 32, 1420-1431.</span></p> <p><span><span> </span></span><span>Maxbauer, D.P., Feinberg, J.M., Fox, D.L., 2016. MAX UnMix: A web application for unmixing magnetic coercivity distributions. <em>Comput. Geosci.</em> 95, 140–145. https://doi.org/10.1016/j.cageo.2016.07.009</span></p>
Cumulative arrival time distribution data for "Upscaling transport in heterogeneous media featuring local-scale dispersion: flow channeling, macro-retardation and parameter prediction"
<div> <div>This archive contains arrival time CDF data for a variety of transport simulations in heterogeneous Darcy flow fields, alongside metadata describing the flow fields. The flow fields were spatially periodic, intersected by uniformly-spaced imaginary planes. Arrival times represent length of time from particle departure from one plane until arrival at the next.</div> <div> </div> <div>Consult the README.md file at the top level of the archive for more information. The file format used to store the CDF data is documented in the Python script at the top level of the archive.</div> </div>
Wall collision of deformable bubbles in the creeping flow regime (Supporting Data)
<p>The dataset contains sample numerical results of a bubble colliding with a solid wall at small Reynolds number, pertaining to the manuscript under the same title, "Wall collision of deformable bubbles in the creeping flow regime", published in the European Journal of Mechanics - B/Fluids.</p>
Biofilm flow data from: Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics
<p>This dataset contains the instantaneous velocity vector fields as well as the time averaged velocity and turbulence fields from PIV data taken over a large acrylic plate fouled with a relatively uniform diatomaceous biofilm. </p> <p>See associated article, Roughness effects of diatomaceous slime fouling on turbulent boundary layer hydrodynamics, for methods description.</p> <p>Time averaged velocity and turbulence fields data are stored in the file velocity_fields.mat, which contains the following variables: </p> <p>X: The streamwise distance of each column in the velocity field matrices [mm]</p> <p>Y: The vertical distance (from the bottom of the frame) of each row in the velocity field matrices [mm]</p> <p>U: Time averaged streamwise velocity [m s^-1]</p> <p>V: Time averaged vertical velocity [m s^-1]</p> <p>tke: Time averaged turbulent kinetic energy [m^2 s^-2]</p> <p>u': Time averaged streamwise Reynolds stress [m^2 s^-2]</p> <p>v': Time averaged vertical Reynolds stress [m^2 s^-2]</p> <p>u'v': Time averaged Reynolds shear stress [m^2 s^-2]</p> <p>The zip file biofilm_vector_fields contains the 4,000 statistically independent velocity vector fields used to compute the time averaged velocity and turbulence fields. The vector field data is in the variable labeled matr. Size calibration: 2302 pixels/ inch (906.3 pixels/ cm). </p> <p>Column 1: X (streamwise distance in [pixels]) </p> <p>Column 2: Y (wall-normal distance from bottom of frame in [pixels])</p> <p>Column 3: U (streamwise velocity vector in [pixels / 250 microseconds]) </p> <p>Column 4: V (vertical velocity vector in [pixels / 250 microseconds]) </p> <p>Column 5: CHC (number of tracked particles. A value < 1 gives the location of the biofilm, which was masked out)</p> <p>Column 6: U2 </p> <p>Column 7: V2</p> <p>Column 8: U3</p> <p>Column 9: V3</p> <p>Column 10: U4</p> <p>Column 11: V4</p>
Approximate solutions for ideal dam-break sediment-laden flows on uniform slopes: Data deposit
<p>This data deposit contains all the datasets needed to draw Figures 3-18 in the paper "Approximate solutions for ideal dam-break sediment-laden flows on uniform slopes", which is now under consideration for publication in Water Resources Research. </p>
Data file for paper: Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries, Electrochemistry Communications, 2018
<p>Excel Data file containing the data presented in the figures of the paper:</p> <p>Javier Rubio-Garcia; Anthony R J Kucernak, and Alexandra Charleson, "Direct visualization of reactant transport in forced convection electrochemical cells and its application to Redox Flow Batteries</p> <p>Electrochemistry Communications, 2018,</p> <p>DOI:10.1016/j.elecom.2018.07.002</p> <p>Please cite the above reference if you wish to use this data</p>
Scalability Measurement Data for "Efficient Data Flow Constraint Analysis" Master's Thesis
<p>Raw measurement data and plots of performed scalability measurments of our proposed approach for analyzing software systems regarding data flow constraints.</p>
Data file for paper: Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Rubio Garcia, Javier; Kucernak, Anthony; Zhao, Dong; Li, Danlei; Fahy, Kieran; Yufit, Vladimir; Brandon, Nigel; Gomez-Gonzalez, Miguel, "Hydrogen/manganese hybrid redox flow battery", Journal of Physics: Energy, 2018</p> <p>DOI: 10.1088/2515-7655/aaee17 </p> <p>Please cite the above reference if you wish to use this data</p>
Data for: Projected effects of climate-change-induced flow alterations on stream macroinvertebrate abundances
<p>Global change has the potential to affect river flow conditions which are fundamental determinants of physical habitats. Predictions of the effects of flow alterations on aquatic biota have mostly been assessed based on species ecological traits (e.g., current preferences), which are difficult to link to quantitative discharge data. Alternatively, we used empirically derived predictive relationships for species’ response to flow to assess the effect of flow alterations due to climate change in two contrasting central European river catchments. Predictive relationships were set up for 294 individual species based on (1) abundance data from 223 sampling sites in the Kinzig lower-mountainous catchment and 67 sites in the Treene lowland catchment, and (2) flow conditions at these sites described by five flow metrics quantifying the duration, frequency, magnitude, timing and rate of flow events using present-day gauging data. Species’ abundances were predicted for three periods: (1) baseline (1998–2017), (2) horizon 2050 (2046–2065) and (3) horizon 2090 (2080–2099) based on these empirical relationships and using high-resolution modeled discharge data for the present and future climate conditions. We compared the differences in predicted abundances among periods for individual species at each site, where the percent change served as a proxy to assess the potential species responses to flow alterations. Climate change was predicted to most strongly affect the low-flow conditions, leading to decreased abundances of species up to −42%. Finally combining the response of all species over all metrics indicated increasing overall species assemblage responses in 98% of the studied river reaches in both projected horizons and were significantly larger in the lower-mountainous Kinzig compared to the lowland Treene catchment. Such quantitative analyses of freshwater taxa responses to flow alterations provide valuable tools for predicting potential climate-change impacts on species abundances and can be applied to any stressor, species, or region.</p>
Quality check of river flow data worldwide
Quality characteristics for 21586 river flow time series from 13 datasets worldwide. The 13 datasets are: the Global Runoff Database from the Global Runoff Data Center (GRDC), the Global River Discharge Data (RIVDIS; Vörösmarty et al., 1998), Surface-Water Data from the United States Geological Survey (USGS), HYDAT from the Water Survey of Canada (WSC), WISKI from the Swedish Meteorological and Hydrological Institute (SMHI), Hidroweb from the Brazilian National Water Agency (ANA), National data from the Australian Bureau of Meteorology (BOM), Spanish river flow data from the Ecological Transition Ministry (Spain), R-ArcticNet v. 4.0 from the Pan-Arctic Project Consortium (R-ArcticNet), Russian River data (NCAR-UCAR; Bodo, 2000), Chinese river flow data from the China Hydrology Data Project (CHDP; Henck et al., 2010, 2011), the European Water Archive from GRDC - EURO-FRIEND-Water (EWA), and the GEWEX Asian Monsoon Experiment (GAME) – Tropics dataset provided by the Royal Irrigation Department of Thailand. Quality characteristics are based on availability, outliers, homogeneity and trends: overall availability (%), longest availability (%), continuity (%), monthly availability (%), outliers ratio (%), homogeneity of annual flows (number of statistical tests agreeing), trend in annual flows, trend in one month of the year. Bodo, B. (2000) Russian River Flow Data by Bodo. Boulder CO: Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory. Retrieved from http://rda.ucar.edu/datasets/ds553.1/ Henck, A. C., Huntington, K. W., Stone, J. O., Montgomery, D. R. & Hallet, B. (2011) Spatial controls on erosion in the Three Rivers Region, southeastern Tibet and southwestern China. Earth and Planetary Science Letters 303(1–2), 71–83. doi:10.1016/j.epsl.2010.12.038 Henck, A. C., Montgomery, David R., Huntington, K. W. & Liang, C. (2010) Monsoon control of effective discharge, Yunnan and Tibet. Geology 38(11), 975–978. doi:10.1130/G31444.1 Vörösmarty, C. J., Fekete, B. M. & Tucker, B. A. (1998) Global River Discharge, 1807-1991, V[ersion]. 1.1 (RivDIS). doi:10.3334/ornldaac/199
Supporting data and code for: "Global unsustainable virtual water flows in agricultural trade"
<p>Supporting data and code for: "<strong>Global unsustainable virtual water flows in agricultural trade"</strong></p> <p>This file contains:</p> <p>-The code used to process trade and production data.</p> <p>- Crop- and country-specific unsustainable virtual water flows for years 2000 and 2015.</p> <p>-Crop- and country-specific sustainable and unsustainable irrigation water consumption.</p> <p> </p>
Data sets for the Publication 'Analytical solution of gas flow in rough-walled micro-fracture at in-situ state' in Water Resources Research.
<p>Data sets for the Publication 'Analytical solution of gas flow in rough-walled micro-fracture at in-situ state' in Water Resources Research.</p>
A passenger flow data set collected in the metro system of Hangzhou, China
<p>This repository is a passenger flow (mobility) data set collected in the Hangzhou metro system with 81 stations.</p> <p><strong>Note</strong>: The source data of this repository is from <strong><a href="https://tianchi.aliyun.com/competition/entrance/231708/information">Urban computing data set - Tianchi competition</a></strong>.</p>
Data from "Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering"
<p>Data from "Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering" published in PNAS.</p>
Raman data for "Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification"
<p>This file contains the Raman data and code for "Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification" by Comeau et al. in Scientific Reports. Run the file, "run.R" in R to reproduce the analysis.</p> <p>Please see the published paper for methods and details: https://doi.org/10.1038/s41598-019-49044-w</p>
Data for the article "A fully-coupled algorithm with implicit surface tension treatment for interfacial flows with large density ratios"
<p>This folder contains representative data generated for each case demonstrated in the paper entitled "A fully-coupled algorithm with implicit surface tension treatment for interfacial flows with large density ratios".<br>Authors: Romain Janodet, Berend van Wachem, and Fabian Denner.</p> <p>A readme file is included to help the navigation within the folder.</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant numbers 452036112, 452916560, and 458610925.</p>
Data for: An Imaging Scheme to Study Chaotic Flow in Co-Flow Microfluidics: Implications for Nanoprecipitation
<p>This dataset includes the raw data and analysis of the mixing flow of water and ethanol in microfluidics device using imaging approach.</p>
Fig. 2. A–G, Aphaena discolor. A–B in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 2. A–G, Aphaena discolor. A–B, specimen on his host-tree, Kirirom, 9.V.2015 (J. Constant). C, habitat in Kirirom, 9.V.2015 (J. Constant). D, Pursat, Cardamom, 2.I.2009 (J. Holden). E, Koh Kong, Tatai, 5.III.2012 (G. Chartier). F–G, Dichoptera sp. Chambok, 5.V.2015 (J. Constant). H, Kalidasa nigromaculata, Siem Reap, Angkor, 10.VIII.2014 (S. De Greef). I, Penthicodes atomaria tended by a cockroach, Cardamom Mts, 4.VIII.2013 (A. Anker). J, P. pulchella, Siem Reap, 18.IX.2013 (S. De Greef). K, P. variegata, Mondulkiri, O Reang District, 19.V.2015 (B. Barca). L–M, Polydictya tricolor, Siem Reap, Angkor, 1.VIII.2013 (S. De Greef). N–O, Polydictya sp., 8 km NNW Angkor, 6.XI.2013 (E. Smith).
Fig. 1 in Updating lanternflies biodiversity knowledge in Cambodia (Hemiptera: Fulgoromorpha: Fulgoridae) by optimizing field work surveys with citizen science involvement through Facebook networking and data access in FLOW website
Fig. 1. Call to collaboration to the study of Fulgoridae of Cambodia posted on Facebook on May 18th, 2015.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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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.