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5,371 results for “Flow”
Along flow acceleration of the Greenland ice sheet
<p>The acceleration of Greenland ice flow derived from ITS_LIVE annual velocity data spanning 1985-2018.</p> <p>There are two variations of using either weighted and unweighted least squares in the estimation. The data files follow the format:</p> <ul> <li>weighted--ax.tif | acceleration in the x direction:</li> <li>weighted--ay.tif | acceleration in the y-direction.</li> <li>weighted--a.tif | acceleration in the dominant flow direction</li> <li>weighted--asigma.tif | standard error estimate of the "weighted--a" data.</li> <li>weighted--N.tif | Number of years with data for each grid point.</li> </ul> <p>All data are using a polar stereographic projection (EPSG:3413). </p> <p> </p> <p>This dataset was created as part of the study: Grinsted et al. 2022, Accelerating ice flow at the onset of the Northeast Greenland ice stream. Processing choices is detailed there. </p>
Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile
<p>The diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore-network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.</p>
Flow map data of the singel pendulum, double pendulum and 3-body problem
<p>This dataset was constructed to compare the performance of various neural network architectures learning the flow maps of Hamiltonian systems. It was created for the paper: <a title="Paper" href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4555181" target="_blank" rel="noopener">A Generalized Framework of Neural Networks for Hamiltonian Systems</a>.</p> <p>The dataset consists of trajectory data from three different Hamiltonian systems. Namely, the single pendulum, double pendulum and 3-body problem. The data was generated using numerical integrators. For the single pendulum, the symplectic Euler method with a step size of 0.01 was used. The data of the double pendulum was also computed by the symplectic Euler method, however, with an adaptive step size. The trajectories of the 3-body problem were calculated by the arbitrarily high-precision code <a title="Brutus" href="https://comp-astrophys-cosmol.springeropen.com/articles/10.1186/s40668-014-0005-3" target="_blank" rel="noopener">Brutus</a>.</p> <p>For each Hamiltonian system, there is one file containing the entire trajectory information (*_all_runs.h5.1). In these files, the states along all trajectories are recorded with a step size of 0.01. These files are composed of several <a title="Pandas" href="https://pandas.pydata.org/" target="_blank" rel="noopener">Pandas</a> DataFrames. One DataFrame per trajectory, called "run0", "run1", ... and finally one large DataFrame in which all the trajectories are combined, called "all_runs". Additionally, one Pandas Series called "constants" is contained in these files, in which several parameters of the data are listed.</p> <p>Also, there is a second file per Hamiltonian system in which the data is prepared as features and labels ready for neural networks to be trained (*_training.h5.1). Similar to the first type of files, they contain a Series called "constants". The features and labels are then separated into 6 DataFrames called "features", "labels", "val_features", "val_labels", "test_features" and "test_labels". The data is split into 80% training data, 10% validation data and 10% test data.</p> <p>The code used to train various neural network architectures on this data can be found on GitHub at: <a title="Code" href="https://github.com/AELITTEN/GHNN" target="_blank" rel="noopener">https://github.com/AELITTEN/GHNN</a>.</p> <p>Already trained neural networks can be found on GitHub at: <a title="NeuralNets_GHNN" href="https://github.com/AELITTEN/NeuralNets_GHNN" target="_blank" rel="noopener">https://github.com/AELITTEN/NeuralNets_GHNN</a>.</p> <table> <tbody> <tr> <td> </td> <td><strong>Single pendulum</strong></td> <td><strong>Double pendulum</strong></td> <td><strong>3-body problem</strong></td> </tr> <tr> <td>Number of trajectories</td> <td>500</td> <td>2000</td> <td>5000</td> </tr> <tr> <td>final time in all_runs</td> <td>T (one period of the pendulum)</td> <td>10</td> <td>10</td> </tr> <tr> <td>final time in training data</td> <td>0.25*T</td> <td>5</td> <td>5</td> </tr> <tr> <td>step size in training data</td> <td>0.1</td> <td>0.1</td> <td>0.5</td> </tr> </tbody> </table> <p> </p>
Peak Flow Event Durations in the Mississippi River Basin and Implications for Temporal Sampling of Rivers
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This repository corresponds to all the input and output files that were used in the study reported in:</p> <ul> <li>Cerbelaud, A., David, C. H., Biancamaria, S., Wade, J., Tom, M., Prata de Moraes Frasson, R., & Blumstein, D. (2024). Peak flow event durations in the Mississippi River basin and implications for temporal sampling of rivers. Geophysical Research Letters, 51, e2024GL109220. <a href="https://doi.org/10.1029/2024GL109220" target="_blank" rel="noopener">https://doi.org/10.1029/2024GL109220</a>.</li> </ul> <p>When making use of any of the output files of this dataset, please cite both the aforementioned article and the dataset herein. </p> <p><strong>Main goals of the publication</strong></p> <p>The corresponding work aims to quantify peak flow event durations at an hourly time scale and their impact on high-frequency river sampling requirements using sampling ratios. The analysis is performed over the Mississippi basin using hourly USGS gages over 2010-2022.</p> <p>The findings derived from these output files have direct implications for future satellite missions concerned with capturing high-frequency dynamics in rivers, including flood events.</p>
HipFT Sample Input Dataset for Convective Flows and Data Assimilation
<p>This file package is a sample data set for running <a href="https://www.github.com/predsci/hipft">HipFT</a> with convective flows and data assimilation. </p> <p>The convective flows were generated with the <a href="https://www.github.com/predsci/conflow">ConFlow</a> code (soon to be released), while the data assimilation maps were processed from HMI M720s LOS data using the <a href="https://www.github.com/predsci/MagMAP">MagMAP</a> package (also soon to be released).</p> <p>See the enclosed README file on how to run an example included in the HipFT package that uses the two data sets.</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophobic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
Molecular simulations of nanoscale two-phase Couette flow of a water-hexane system on a hydrophilic substrate
<p>This dataset contains the output of Molecular Dynamics simulations (MD) of two-phase Couette flow of water/hexane biphasic systems, in terms of density, velocity and temperature fields. Instructions on how to read and analyze the output files in the <code>.tar.gz</code> archives can be found in these previously-published datasets: <a href="https://doi.org/10.5281/zenodo.8077915">https://doi.org/10.5281/zenodo.8077915</a>, <a href="https://doi.org/10.5281/zenodo.6541983">https://doi.org/10.5281/zenodo.6541983</a></p> <p>The run output files are labeled using the following pattern: <code>hex-ca<capillary-number>-q<partial-charge>.tar.gz</code>. It is possible to obtain the wall speed/contact line speed from the capillary number using the following formula: <code>u_w = U_0*<capillary-number></code>, with <code>U_0 = 37.246 m/s</code>.</p> <p>To reproduce the runs it is necessary to use a specific version of Gromacs that allows for a special algorithm of pressure scaling with position restraints. The code can be obtained by cloning <a href="https://github.com/MicPellegrino/gromacs-flow-field.git">https://github.com/MicPellegrino/gromacs-flow-field.git</a>, and switching to the <code>flow-field-grid-visco-coms-deform</code> branch.</p> <p>The folder <code>conf-wat-hex.zip</code> contains the configuration files to reproduce MD simulations. To prepare the equilibration runs at constant pressure, run after having installed Gromacs:</p> <p><code>gmx grompp -f npt.mdp -p topology.top -c before-npt.gro -r before-npt.gro -o system-npt.tpr</code></p> <p>while to prepare the shear runs:</p> <p><code>gmx grompp -f shear.mdp -p topology.top -c after-npt.gro -r lambda0.gro -rb lambda1.gro -o system-shear.tpr</code></p> <p>Simulations are launched by running:</p> <p><code>gmx mdrun -v -s <tpr-file-name>.tpr <possibly-other-mdrun-flags></code></p> <p>Have fun simulating!</p>
FLOW-Alaiz Benchmark: Baseline Results
<p>This repository hosts documentation and notebooks for the <a href="https://www.flow-horizon.eu/">EU-FLOW</a> Alaiz benchmark.</p> <p>The first stage is finished with the publication of baseline results for PyWAsP, PyWAsP-CFD, and SiteFlow in the Torque-2024 conference.</p> <p><strong>Sanz Rodrigo J, Oxley G and Tobias Olsen B (2024) FLOW-Alaiz benchmark for coupled terrain and array interaction flow models. Baseline Results. J. Phys.: Conf. Ser. 2767 092077, <a href="https://iopscience.iop.org/article/10.1088/1742-6596/2767/9/092077">doi:10.1088/1742-6596/2767/9/092077</a></strong></p>
Detailed sap flow monitoring data at Weierbach catchment, Luxembourg
<p>The current repository contains the dasometric data of monitored trees, hourly sap flow data, and tree surveys carried out at the Weierbach catchment during 2019 and 2020. The monitored trees include the following species:</p> <p>- Beech (<em>Fagus sylvatica </em>L.)</p> <p>- Douglas Fir (<em>Pseudotsuga menziesii</em> (Mirbel) Franco)</p> <p>- Spruce (<em>Picea abies </em>L.)</p> <p>- Oak (hybrids of <em>Quercus petraea</em> (Matt.) Liebl. and <em>Quercus robur </em>L.)</p> <p>The sap flow data for all the trees is presented in cm/hr.</p> <p>The bark data presented in the "dasometry_individual_trees.csv" was obtained from the literature.</p>
Flow cytometry of mesenteric lymph nodes, small and large intestinal lamina propria, and spinal cord cells from fibre-rich and fiber-free diet-fed gnotobiotic mice at baseline and after experimental autoimmune encephalomyelitis (EAE) induction
<p>We perform profiling of different immune cell populations in the small (SILP) and large intestine lamina propria (CLP), mesenteric lymph nodes (MLN) and spinal cords (SC). We are specifically interested to evaluate the impact of dietary fiber deprivation followed by mucus erosion on the immune cell profiles of T helper cells (Th cells, T cell population) of gnotobiotic mice fed a fiber-rich (FR) or fiber-free (FF) diet. This dataset aims to assess the impact of microbiome and diet on disease course in a mouse model of multiple sclerosis (experimental autoimmune encephalomyelitis, EAE) via T cell populations. Mice are either germ-free or colonized by intragastric gavage with a defined variation of a 14-member synthetic human gut microbiome (doi: 10.1016/j.cell.2016.10.043 and 10.1016/j.xpro.2021.100607): SM01 (Akkermansia muciniphila monocolonisation), SM03 (Bacteroides caccae, Bacteroides thetaiotaomicron, Barnesiella intestinihominis), SM04 (B. caccae, B. thetaiotaomicron, B. intestinihominis, A. muciniphila), SM12 (full community except mucin-specialists B. intestinihominis and A. muciniphila), SM13 (full community except mucin specialist A. muciniphila), or SM14 (full community: Roseburia intestinalis, Faecalibacterium prausnitzii, Marvinbryantia formatexigens, Collinsella aerofaciens, Desulfovibrio piger, B. caccae, B. thetaiotaomicron, Bacteroides ovatus, Bacteroides uniformis, B. intestinihominis, Eubacterium rectale, Clostridium symbiosum, Escherichia coli, and A. muciniphila). At age 5 to 8 weeks, mice were colonized with SM combinations while fed an FR diet. Mice were either maintained on an FR diet or switched to an FF diet at 5 days after initial colonization, until the end of experiment. Baseline samples were collected 20 days following the diet switch. Otherwise, EAE induction was performed 15 days after the diet switch and samples were collected 30 days after the induction.</p>
Dataset for Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread
<p>30 min turbulence statistics and spectra of 13 datasets from flat to highy complex terrain. Data only cover unstable stratification. </p> <p>Dataset is a companion to the manuscript Charrondiere, C., Stiperski, I., 2024: Spectral scaling of unstably-stratified atmospheric flows: turbulence anisotropy and the low frequency spread. Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4811<strong><br></strong></p> <p> </p>
Sample Records (PRISMA Checklist and Flow diagram): Disinformation as a strategy of obstructionism on climate action: analysis of the limitations of the scientific literature for a systemic understanding of the phenomenon
<p>This dataset includes: the PRISMA Checklist and the <span>PRISMA Flow diagram of the study titled: Disinformation as an obstructionist strategy in climate change mitigation: A review of the scientific literature for a systemic understanding of the phenomenon.</span></p>
Supporting Datasets produced in Allen et al. (2018) Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data"
<p><strong>Supporting datasets for Allen et al. (2018) - Global Estimates of River Flow Wave Travel Times and Implications for Low-Latency Satellite Data, <em>Geophysical Research Letters</em>, <a href="https://doi.org/10.1002/2018GL077914">https://doi.org/10.1002/2018GL077914</a></strong></p> <p>The code used to produce these data is available as a Github repository, permanently hosted on Zenodo: <a href="https://doi.org/10.5281/zenodo.1219784">https://doi.org/10.5281/zenodo.1219784</a></p> <p><strong>Abstract</strong></p> <p>Earth-orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real-time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet, the temporal requirements for access to satellite-based river data remain uncharacterized for time-sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low-latency/near-real-time satellite products, with an emphasis on the forthcoming SWOT satellite. We apply a kinematic wave model to a global hydrography dataset and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4 and 3 days to reach their basin terminus, the next downstream city and the next downstream dam respectively. Our findings suggest that a recently-proposed ≤2-day latency for a low-latency SWOT product is potentially useful for real-time river applications.</p> <p> </p> <p><strong>Description of repository datasets:</strong></p> <p>1. riverPolylines.zip contains ESRI shapefile polylines of river networks with outputs from main analysis. These continental-scale shapefiles contain the following attributes for each river segment:</p> <ul> <li>"ARCID" : unique identifier for each river segment line, defined as the river reach between river junctions/heads/mouths. The first 10 attributes are taken from Andreadis et al. (2013): https://doi.org/10.5281/zenodo.61758</li> <li>"UP_CELLS" : number of upstream cells (pixels)</li> <li>"AREA" : upstream drainage area (km<sup>2</sup>)</li> <li>"DISCHARGE" : discharge (m<sup>3</sup>/s)</li> <li>"WIDTH" : mean bankfull river width (m)</li> <li>"WIDTH5" : 5th percentile confidence interval bankfull river width (m)</li> <li>"WIDTH95" : 95th percentile confidence interval bankfull river width (m)</li> <li>"DEPTH" : mean bankfull river depth (m)</li> <li>"DEPTH5" : 5th percentile bankfull river depth (m)</li> <li>"DEPTH95" : 95th percentile confidence bankfull river depth (m)</li> <li>"LENGTH_KM" : segment length (km)</li> <li>"ORIG_FID" : original ID of segment</li> <li>"ELEV_M" : lowest elevation of segment (m). Derived from HydroSHEDS 15 sec hydrologically conditioned DEM: https://hydrosheds.cr.usgs.gov/datadownload.php?reqdata=15demg </li> <li>"POINT_X" : longitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"POINT_Y" : latitude of lowest point of segment (WGS84, decimal degrees)</li> <li>"SLOPE" : average slope of segment (m/m)</li> <li>"CITY_JOINS" : an index associated with how likely a city/population center is located on the segment. Population center data from: http://web.ornl.gov/sci/landscan/ and http://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-populated-places/ </li> <li>"CITY_POP_M" : population of joined city (max N inhabitants) </li> <li>"DAM_JOINSC" : an index associated with how likely a dam is located on the segment. Dam data from Global Reservoir and Dam (GRanD) Database: http://www.gwsp.org/products/grand-database.html </li> <li>"DAM_AREA_S" : surface area of joined dam (m<sup>2</sup>)</li> <li>"DAM_CAP_MC" : volumetric capacity of joined dam (m<sup>3</sup>)</li> <li>"CELER_MPS" : modeled river flow wave celerity (m/s)</li> <li>"PROPTIME_D" : travel time of flow wave along segment (days)</li> <li>"hBASIN" : main basin UID for the hydroBASINS dataset: http://www.hydrosheds.org/page/hydrobasins</li> <li>"GLCC" : Global Land Cover Characterization at segment centroid: https://lta.cr.usgs.gov/glcc/globdoc2_0 </li> <li>"FLOODHAZAR" : flood hazard composite index from the DFO (via NASA Sedac): http://sedac.ciesin.columbia.edu/data/set/ndh-flood-hazard-frequency-distribution</li> <li>"SWOT_TRAC_" : SWOT track density (N overpasses per orbit cycle @ segment centroid). Created using SWOTtrack SWOTtracks_sciOrbit_sept15 polygon shapefile, uploaded here.</li> <li>"UPSTR_DIST" : upstream distance to the basin outlet (km) </li> <li>"UPSTR_TIME" : upstream flow wave travel time to the basin outlet (days)</li> <li>"CITY_UPSTR" : upstream flow wave travel time to the next downstream city (days)</li> <li>"DAM_UPSTR_" : upstream flow wave travel time to the next downstream dam (days)</li> <li>"MC_WIDTH" : mean of Monte Carlo simulated bankfull widths (m)</li> <li>"MC_DEPTH" : mean of Monte Carlo simulated bankfull depths (m)</li> <li>"MC_LENCOR" : mean of Monte Carlo simulated river length correction (km)</li> <li>"MC_LENGTH" : mean of Monte Carlo simulated river length (m)</li> <li>"MC_SLOPE" : mean of Monte Carlo simulated river slope (-)</li> <li>"MC_ZSLOPE" : mean of Monte Carlo simulated minimum slope threshold (m)</li> <li>"MC_N" : mean of Monte Carlo simulated Manning’s n (s/m^(1/3))</li> <li>"CONTINENT" : integer indicating the HydroSHEDS region of shapefile</li> </ul> <p>2. hydrosheds_connectivity.zip contains network connectivity CSVs for river polyline shapefiles. The tables do not contain headers:</p> <ul> <li>Col1: segment unique identifier (UID) corresponding to the ARCID column of the riverPolylines shapefiles</li> <li>Col2: Downstream UID</li> <li>Col3: Number of upstream UIDs</li> <li>Col4 – Col12: Upstream UIDs</li> </ul> <p>3. SWOTtracks_sciOrbit_sept15_density.zip contains a polygon shapefile derived from SWOTtracks_sciOrbit_sept15_completeOrbit containing the sampling frequency of SWOT (number of observations per complete orbit cycle). Polygon attributes correspond to each unique shape formed from overlapping swaths:</p> <ul> <li>FID : unique identifier of each polygon</li> <li>CENTROID_X : polygon centroid longitude (WGS84 - decimal degrees)</li> <li>CENTROID_Y : polygon centroid latitude (WGS84 - decimal degrees)</li> <li>COUNT_count: SWOT sampling frequency (N observations per complete orbit cycle)</li> </ul> <p>4. USGS_gauge_site_information.csv : table containing the list of USGS sites analyzed in the validation and obtained from http://nwis.waterdata.usgs.gov/nwis/dv Header descriptions contained within table. </p> <p>5. validation_gaugeBasedCelerity.zip contains polyline ESRI shapefiles covering North and Central America, where USGS gauges provided gauge-based celerity estimates. These files have FIDs and attributes corresponding to riverPolylines shapefiles described above and also contrain the folllowing fields:</p> <ul> <li>GAUGE_JOIN : an index associated with how likely a gauge is located on the segment. Gauge location information is contained in USGS_gauge_site_information.csv</li> <li>GAUGE_SITE: USGS gauge site number of joined gauge</li> <li>GAUGE_HUC8: which hydrological unit code the gauge is located in</li> <li>OBS_CEL_R: gauge-based correlation score (R). Upstream and downstream gauges were compared via lagged cross correlation analysis. The calculated celerity between the paired gauges were assigned to each segment between the two gauges. If there were multiple pairs of upstream and downstream gauges, the the mean celerity value was assigned, weighted by the quality of the correlation, R. Same weighted mean was applied in assigning R. </li> <li>OBS_CEL_MPS: gauge-based celerity estimate (m/s). </li> </ul> <p>6. tab1_latencies.csv contains data shown in Table 1 of the manuscript.</p> <p>7. figS3S4_monteCarloSim_global_runMeans.csv contains the mean of the Monte Carlo simulation inputs and outputs shown in Figure S3 and Figure S4. Column headers descriptions are given in riverPolylines (dataset #1 above). Some columns have rows with all the same value because these variables did not vary between ensemble runs.</p> <p>8. figS5_travelTimeEnsembleHistograms.zip contains data shown in Figure S5. Each csv corresponds to a figure component:</p> <ul> <li>tabdTT_b.csv : basin outlet travel times for all rivers</li> <li>tabdTT_b_swot.csv : basin outlet travel times for SWOT</li> <li>tabdTT_c.csv : next downstream city travel times for all rivers</li> <li>tabdTT_c_swot.csv : next downstream city travel times for SWOT</li> <li>tabdTT_d.csv : next downstream dam travel times for all rivers</li> <li>tabdTT_d_swot.csv : next downstream dam travel times for SWOT</li> </ul>
Accompanying dataset for: "Flow and detailed 3D morphodynamic data from laboratory experiments of fluvial dike breaching"
<p>This dataset accompanies the manuscrpit "Flow and detailed 3D morphodynamic data from laboratory experiments of fluvial dike breaching" submitted to Scientific Data.</p>
Database of Nightside, High-latitude Ionosphere Meso-scale Flow Characteristics
<p>This database is a compilation of nightside, high-latitude ionosphere meso-scale flow characteristics built on those used in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). It is the most complete version. If you would like to use the database, please contact Christine Gabrielse (cgabrielse@ucla.edu, cgabrielse@gmail.com, and/or christine.gabrielse@aero.org). Depending on how the results are used, the main authors request co-authorship on publications that utilize this database. </p> <p>The methodology and selection criteria can be found in Gabrielse et al. [2018] (<a href="https://doi.org/10.1029/2018JA025440">https://doi.org/10.1029/2018JA025440</a>). </p> <p>The following list describes the columns in each data file labeled, ***_FLOW-DATA-PCvsAO_YYYY.txt <br> The first three letters (RNK or SAS) designate the station used (Rankin Inlet or Saskatoon).<br> Files named ***_FLOW-DATA-PCvsAO_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> <br> AO=Auroral Oval for Rankin Inlet; equatorward of the auroral oval for Saskatoon (not used)<br> PC=Polar Cap for Rankin Inlet; Auroral Oval for Saskatoon</p> <p>(Note: the data files for RNK and SAS have the same format, so the PC designator means flows above the pertinent boundary (polar cap boundary for RNK, auroral oval equatorward boundary at SAS) and the AO designator means flows below the pertinent boundary.)</p> <p> time [YYYYMMDDhhmmss]<br> flagAO [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> flagPC [-1=flow could not be observed. 0=flow could be observed, but was not. 1=flow was observed]<br> FWHMavg_AO [degrees]<br> FWHMkmavg_AO=[km]<br> longtestranges=[ignore]<br> Velmaxavg_AO=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_AO=[m/s, determined from the Gaussian fits]<br> FWHMavg_PC=[degrees]<br> FWHMkmavg_PC=[km]<br> Velmaxavg_PC=[m/s, actual average of max V in each range gate used]<br> VelmaxFITavg_PC=[m/s, determined from the Gaussian fits]</p> <p>;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;<br> For the bearings/orientation, see the orientation text files. The following four variables were calculated in a first step but are not<br> those used in the paper. They were not found with the strict selection criteria. **Please do not use.**<br> mbearingAO=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> mbearingPC=[degrees in magnetic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)] <br> gbearingAO=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> gbearingPC=[degrees in geographic coordinates, a negative value is South of East (clockwise from East), a positive value is North of East (CC)]<br> ;;;;;;;;;;;;;;;<br> minlatAO=[degrees, min geographic latitude of the flow]<br> maxlatAO=[degrees, max geographic latitude of the flow]<br> minlatPC=[degrees, min geographic latitude of the flow]<br> maxlatPC=[degrees, max geographic latitude of the flow]<br> mltAO=[degrees (MLT)]<br> mltPC=[degrees (MLT)]<br> AE=[nT]<br> AL=[nT]<br> SYMH=[nT]<br> IMFBy=[nT]<br> IMFBz=[nT] <br> F107=[sfu]</p> <p>The following list describes the columns in each data file labeled, ***_orientation_YYYY.txt <br> Files named ***_orientation_poleward_YYYY.txt are for poleward-directed flows. <br> Each text file is for a different year (YYYY). <br> The orientation was determined when enough bearings between RGs were available. See Gabrielse et al. [2018] for description. <br> https://doi.org/10.1029/2018JA025440 <br> AO=auroral oval<br> PC=polar cap</p> <p> time [YYYYMMDDhhmmss]<br> mbearingAO [degrees clockwise from magnetic North]<br> gbearingAO [degrees clockwise from geographic North]<br> mbearingPC [degrees clockwise from magnetic North]<br> gbearingPC [degrees clockwise from geographic North]</p> <p>The following list describes the columns in each data file labeled, ***_SPEC_TEST_***_noRG1-2.txt</p> <p> time [YYYYMMDDhhmmss]<br> RG [the range gate number at which the polar cap boundary was determined at RNK, or the auroral oval's equatorial boundary at SAS]</p>
Dataset of FEUTURE Online Paper No. 8 "Understanding the EU-Turkey Sectoral Trade Flows During 1990-2016: a Trade Gravity Approach"
<p>The dataset provides the following variables for 1990-2016 for Austria Belgium, Bulgaria, China, Denmark, France, Germany, Greece, Hungary, Iran, Ireland, Italy, Japan, Netherlands, Poland, Russia, Spain, Sweden, UK, and USA: </p> <p>- Total export and import (in USD and %)</p> <p>- Intermediate goods exports and imports (in USD and %)</p> <p>- Household goods exports and imports (in USD and %)</p> <p>- Capital goods exports and imports (in USD and %)</p> <p>- Mixed-end exports and imports (in USD and %)</p> <p>- Miscellaneous exports and imports (in USD and %)</p> <p> </p>
Dataset of FEUTURE Online Paper No. 9 "The Financial Flows and the Future of EU-Turkey Relations"
<p>The dataset provides the annual GDP (in US Dollar) and the annaul GDP growth (in %) of Turkey between 1960 and 2016.</p>
AOSLO Single Cell Blood Flow - Raw Data (eLife paper: Joseph et al. 2019)
<p>Raw AOSLO videos of eLife paper Joseph et al. 2019 - 'Imaging single-cell blood flow in the smallest to largest vessels in the living retina'. <a href="https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.7554_eLife.45077&d=DwMFaQ&c=kbmfwr1Yojg42sGEpaQh5ofMHBeTl9EI2eaqQZhHbOU&r=EdsTL7DuEvOHun7eVBmBd9sxUuPhDEmdFDf0tlkKUO4&m=56MyNrE_d-v6PSU7Go9NePVOrIpAvWHBaC8wVvgs3_k&s=G7SkWT-fSV9d3SJmpWEUUGp2a6mGotbG-uAwNZftpIo&e=">https://doi.org/10.7554/eLife.45077</a> . For additional data or questions, please contact author Aby Joseph (aby.joseph@rochester.edu, dreamworks1991@gmail.com)</p>
Evaluating data-flow coverage in spectrum-based fault localization
<p>This release contains files with the results of the experiment comparing the use of data- and control-flow spectra for Spectrum-based Fault Localization. It also has instructions to run Jaguar to perform experiments. The subject programs used in the experiment are public available in our GitHub repository.</p>
"Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest" -- data sets
<p>These files contain the data used in the analysis and production of graphs reported in a manuscript titled "Effects of forestry on summertime low flows and physical fish habitat in snowmelt-dominant headwater catchments of the Pacific Northwest," by Stefan Gronsdahl, R. Dan Moore, Jordan Rosenfeld, Rich McCleary, Rita Winkler. The paper will be published in the journal Hydrological Processes. The file named "readme.txt" explains the contents of the files.</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.