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5,371 results for “Flow”
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>
GITM simulations about the impact of flow bursts on I/T
<p>Global Ionosphere-Thermosphere model (GITM), a non-hydrostatic model with flexible resolution, has been utilized to evaluate the influence of flow bursts, one type of localized strong enhancements of forcing on the global dynamics of the upper atmosphere. Specifically, three idealized configurations of flow burst have been examined, one flow burst with the typical spatial scale seen in the auroral zone, two neighboring flow bursts with the same spatial scale, and one flow burst with doubled spatial scale. The comparison between the cases with and without the flow burst illustrates the impact of flow burst. </p>
Checking Secure Information Flow of CoCoME using UPPAAL
<p>Model of the CoCoME case study for the UPPAAL model checker. Also includes auto-generated verification models for checking the information flow security of CoCoME, and the corresponding verification properties.</p>
Files for "Ocellaris: a discontinuous Galerkin finite element solver for two-phase flows with high density differences"
<p>Input files and scripts sufficient to reproduce the results shown in the paper "Ocellaris: a discontinuous Galerkin finite element solver for high density ratio two phase flows".</p> <p>A version of Ocellaris similar to version 2019.0.1 was used and all results should be reproducible by this version of Ocellaris. See <a href="https://bitbucket.org/ocellarisproject/ocellaris/">the Ocellaris source code repository</a>, <a href="https://pypi.org/project/ocellaris/2019.0.1/">Ocellaris 2019.0.1 on PyPi</a>, or DOI <a href="https://zenodo.org/record/2558303">10.5281/zenodo.2558303</a>.</p> <p>The Ocellaris user guide can be found at <a href="https://www.ocellaris.org">www.ocellaris.org</a> and it includes guides to how to run Ocellaris in Singularity or Docker containers for ease of installation and testing. Containers for Ocellaris 2019.0.0 are available, for Docker run:</p> <pre><code class="language-bash"># On the host machine: docker run -it trlandet/fenics-dev:2018.1.0.r3 # Inside the Docker container pip3 install ocellaris==2019.0.1 --user </code></pre> <p> </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>
Dataset: Impact of tall buildings and building-height variability on flow within and above a complex urban canopy in London - Part I
<p>This dataset contains the first part of a series of wind-tunnel flow measurements in a 1:200 scale model of a part of the London Borough of Southwark. The data published here accompanies analyses in Hertwig et al. (2019):</p> <p>D. Hertwig, H.L. Gough, S. Grimmond, J.F. Barlow, C.W. Kent, W.E. Lin, A.G. Robins, P. Hayden (2019), "Wake Characteristics of Tall Buildings in a Realistic Urban Canopy", Boundary-Layer Meteorology, DOI: 10.1007/s10546-019-00450-7.</p> <p>Flow measurements (based on 2D Laser Doppler Anemometry) were carried out in the boundary-layer wind-tunnel facility of the Environmental Flow (EnFlo) Research Centre, University of Surrey, UK, using the EPSRC <a href="http://www.magic-air.uk/">MAGIC</a> project model domain and scale model (<a href="https://www.tandfonline.com/doi/full/10.1080/09613218.2018.1468158">Song et al., 2018</a>).</p> <p>This repository contains:</p> <ol> <li>wt_ts_part_I_readme.pdf: Meta information and description of data in 'wt_ts_part_I_data.zip'</li> <li>wt_ts_part_I_data.zip: Flow measurements (time series and statistics)</li> </ol>
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>
Dataset for: Motile cilia hydrodynamics: Entrainment versus synchronisation when coupling through flow
<p>This archive contains the dataset for the article "Motile cilia hydrodynamics: Entrainment versus synchronisation when coupling through flow".</p> <p>We gathered videos of cilia dynamics from different sources. For each animal/microorganism we upload the videos in different formats depending on the source:</p> <p>- .tiffs usually in a directory, it is composed by a series of images</p> <p>- .movie a format that we use in our lab and that it is combatible with our software in Matlab to tack cilium waveform</p> <p>- .avi </p> <p>For each video we also have the results from the tracking with our software. The resuts from a single video are all in a Matlab variable that has the same name but with the extension .clclk_force</p> <p>for example for the video "filename", the results are in "filename.clclk_force".<br> In Matlab these can be loaded as force = load('filename.clclk_force','-mat');<br> For all the videos these can be directly be found in the directory cilium waveforms force files.</p>
Fig. 2 in Using haplotype networks, estimation of gene flow and phenotypic characters to understand species delimitation in fungi of a predominantly Antarctic Usnea group (Ascomycota, Parmeliaceae)
Fig. 2 Enlargement of the Usnea aurantiaco-atra group of the Bayesian inference (Fig. 1) depicting 101 taxa. Posterior probabilities ≥ 0.95 are visualized by bold branches. Colors match the sampling site colours in Fig. 4a. A! fertile specimen with apothecia, S! vegetative reproduction via soralia. Most important clades of the nested clade analysis (Fig. 4a,b) are plotted on the phylogenetic tree
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. 4 in Going with the Flow: Testing the Role of Habitat Isolation among Three Ecologically Divergent Darter Species
FIG. 4. Reduced major axis regression plots from the isolation by distance analysis for (A) Ammocrypta beanii, (B) Etheostoma swaini, and (C) Percina nigrofasciata.
FIG. 3 in Going with the Flow: Testing the Role of Habitat Isolation among Three Ecologically Divergent Darter Species
FIG. 3. Bar plots representing estimated genetic clusters (K) and individual probability estimates of cluster assignment (Q values) from program STRUCTURE for (A) A. beanii at K ¼ 5, (B) E. swaini at K ¼ 2, and (C) P. nigrofasciata at K ¼ 4. The most likely K value was estimated using the ad hoc DK statistic (Evanno et al., 2005).
FIG. 2 in Going with the Flow: Testing the Role of Habitat Isolation among Three Ecologically Divergent Darter Species
FIG. 2. Chart showing habitat characteristics of each darter species and predicted levels of gene flow between populations based on habitat variables. FST represents population differentiation, while AR represents allelic richness. Arrows pointing up indicate an increase, arrows pointing down a decrease, and horizontal arrows indicate moderate/intermediate levels. Illustrations were designed by Elizabeth Marchio (elizabeth. marchio@gmail.com; www.lizmarchio.com).
FIG. 1 in Going with the Flow: Testing the Role of Habitat Isolation among Three Ecologically Divergent Darter Species
FIG. 1. Map of Pearl River including sampling sites for the three darter species. Ammocrypta beanii, Etheostoma swaini, and Percina nigrofasciata are represented by circles, squares, and diamonds, respectively. Map was generated using ArcGIS ArcMap 10.
Flow fields of simple cases with various configurations of eddies and background flows
<p>This archive corresponds to the data utilized in the manuscript submitted to <em>Geophysical Research Letters</em> [2024GL111599].</p> <p>The data are compatible with GrADS and other tools.</p> <p>Please see the included README.pdf file for details about each data file.</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.