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5,371 results for “Flow”
Water flow velocity data, Shark River Slough (SRS) near Frog City, south of US 41, Everglades National Park (FCE LTER) from October 2006 to July 2009
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Frog City jetty, Everglades National Park, using Sontek Agronaut water flow sampler.
Water flow velocity data, Shark River Slough (SRS) near Gumbo Limbo Island, Everglades National Park (FCE) from October 2003 - December 2018
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Gumbo Limbo Island, Everglades National Park, using Sontek Agronaut water flow sampler. Data collection is complete.
Water flow velocity data, Shark River Slough (SRS) near Satinleaf Island, Everglades National Park (FCE LTER) from July 2003 to December 2005
Water velocity data measured every 5 or 15 minutes in Shark River Slough near Satinleaf tree island, Everglades National Park, using Sontek Agronaut water flow sampler.
Raw data for: Pressure and inertia sensing drifters for glacial hydrology flow path measurements
<p>Raw data for paper</p> <p>Title: Pressure and inertia sensing drifters for glacial hydrology flow path measurements</p> <p>Authors: A.Alexander, M.Kruusmaa, J.A. Tuhtan, A.J. Hodson, T.V. Schuler, A. Kääb</p> <p>Journal: The Cryosphere</p> <p>Year, 2020</p>
Cyclist Actions: Optical Flow Sequences and Trajectories
<p>The dataset consists of over 1.1 million samples of labeled cyclists actions. Every sample consists of two optical flow sequences, recorded over the past second (9 optical flow images each), from two different cameras, the past trajectory of the cyclist of the last second (50 past positions), and a label of the currently performed action.</p> <p>The samples were extracted from 1,639 video sequences of cyclists moving across an urban intersection at the University of Applied sciences in Aschaffenburg: <a href="https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/">https://www.th-ab.de/ueber-uns/organisation/labor/kooperative-automatisierte-verkehrssysteme/ausstattung/</a></p> <p>The uploaded files consist of an archive containing 27 numpy files, a single numpy file containing trajectories only, and a json file containing 5-fold cross validation/test split.</p> <p>The numpy files consist of python dictionaries with scenes of the form:</p> <pre><code class="language-python">{SCENE_NAME: 'of_hk1/2': [...], # zip compressed, python pickled optical flow sequences of cameras 1/2 'x/y/z_tracked': [...], # tracked cyclists positions in x/y/z directions, 'x/y/z_smoothed': [...], # smoothed (by rts smoother) cyclists positions in x/y/z directions, 'orientation': [...], # orientation of the cyclists estimated by kalman filters 'ts': [...], # utc timestamps in micro seconds LABEL_NAME: [...], # labels of different actions (0 or 1)}</code></pre> <p>The manually created labels are:</p> <ul> <li>straight: cyclists is moving and not turning</li> <li>tr/tl: cyclist is turning left/right</li> <li>move: cyclist is moving with nearly constant velocity and not turning</li> <li>start: cyclist was standing and starts moving</li> <li>starting_movement: first movement of cyclist before starting</li> <li>stop: cyclist was moving/starting and slows down to a halt</li> <li>wait: cyclist is standing</li> <li>hand_signal_left/right: cyclist indicates a turn by hand signal</li> <li>shoulder_check_left/right: cyclist looks over left/right shoulder</li> <li>out_of_saddle: cyclist is standing</li> </ul> <p>The optical flow sequences were created using PWC-Net [1].</p> <p>To extract the zipped/pickled optical flow sequences:</p> <pre><code class="language-python">import cv2 as cv import zlib import pickle import numpy as np # visualize flow def vis_of(of): hsv = np.zeros([of.shape[0], of.shape[1], 3], dtype=np.uint8) hsv[..., 1] = 255 mag, ang = cv.cartToPolar(of[..., 0].astype(np.float32), of[..., 1].astype(np.float32)) hsv[..., 0] = ang * 180 / np.pi / 2 hsv[..., 2] = cv.normalize(mag, None, 0, 255, cv.NORM_MINMAX) bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR) return bgr # load npy file from dataset npy_path = 'of_dataset_0.npy' data = np.load(npy_path, allow_pickle=True).item() scene = data[list(data.keys())[0]] # extract optical flow sequence ofs = pickle.loads(zlib.decompress(scene['of_hk1'][i])).astype(np.float16) * 2.0 / 255.0 - 1.0 # show of images in sequence for j in range(len(ofs)): # create bgr image from 2 channel optical flow bgr = vis_of(ofs[j]) cv.imshow("of", bgr) </code></pre> <p>Python code and a description to read the dataset can be found in our GitHub: <a href="https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition">https://github.com/CooperativeAutomatedTrafficSystemsLab/CyclistActionRecognition</a></p> <p>[1] D. Sun, X. Yang, M. Liu, and J. Kautz, “PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, June 2018, pp. 8934–8943.</p> <p> </p> <p>This work results from the project DeCoInt 2, supported by the German Research Foundation (DFG) within the priority program SPP 1835: "Kooperativ interagierende Automobile", grant numbers DO 1186/1-2, FU 1005/1-2, and SI 674/11-2. Additionally, the work is supported by "Zentrum Digitalisierung Bayern".</p> <p>Due to privacy laws in germany, we are not permitted to publish image sequences.</p>
Simulated river flow and temperature in regulated river systems in the southeastern United States
<p>Streamflow and stream temperature are important water resources variables, and regional-scale simulations are essential for water resources management and multi-sector assessment for large regions, e.g., regional ecological assessment and power system planning. In large-scale stream temperature modeling practices, reservoir thermal stratification is mostly ignored. We have synthesized a process-based modeling approach, consisting of a series of established models, to simulate streamflow and stream temperature for a complicated river-reservoir system, which explicitly considers thermal stratification. This approach consists of a large-scale, spatially-distributed hydrological model (Variable Infiltration Capacity or VIC; Liang et al., 1994; Hamman et al., 2018), a river routing model (Model for Scale Adaptive River Transport or MOSART; Li et al., 2013), coupled to a spatially-distributed water management model (WM; Voisin et al., 2013, 2017), and a stream temperature model (River Basin Model or RBM; Yearsley, 2009; 2012) that includes a two-layer reservoir thermal stratification module (2L; Niemeyer et al., 2018). To generate this dataset, we applied this modeling approach at a temporal resolution of 1 day and a spatial resolution of 1/8º to river systems in the southeastern United States that include 271 major reservoirs. We used an ensemble of downscaled meteorological forcing data from 20 global climate models (GCM) based on RCP8.5 to simulate potential climate change impacts. This dataset includes simulated river flow and temperatures for both historical (1980-2009; 1980s) and future periods (2070-2099; 2080s). The simulations for the 1980s are based on the gridMet data set (Abatzolglou, 2013), which also forms the basis for the statistical downscaling that is applied to each of the climate models. All simulations for the 2080s are based on downscaled climate model outputs. This dataset includes streamflow and stream temperature using both unregulated and regulated model setups to quantify the impacts of reservoir regulations. The unregulated setup does not account for withdrawals and impoundments in the river system. The stream temperature in the unregulated model setups is constant in each river cross section. In the regulated setup, we explicitly considered reservoir regulation, thermal stratification, and water withdrawal. For a more detailed description of the model configuration, please see Cheng et al. (2020).</p> <p> </p> <p>File structure and filenames: The archive includes two directories, named “streamflow/” and “stream_temperature/”, which contain model output for streamflow and stream temperature, respectively. Within each directory, subdirectories named “regulated/” and “unregulated/” contain model output for the regulated and unregulated model setups, respectively. All data files are in netCDF format and provide model outputs at a temporal resolution of 1 day and a spatial resolution of 1/8º. The unit for streamflow is m<sup>3</sup>/s and the unit for stream temperature is °C.</p> <p> </p> <p>Files are constructed as follows:</p> <p>SERC.<climate simulation>.RCP85.<model setup>.<variable>.nc</p> <p>where</p> <ul> <li><climate simulation> is either ‘historical’ for the simulation that represents the 1980s or an abbreviation that indicates the climate model for the simulations that represent the 2080s. The abbreviations for the climate models are shown in column 1 in the Table below.</li> <li><model setup> is either ‘regulated’ or ‘unregulated’ for the regulated and unregulated model setups, respectively.</li> <li><variable> is either ‘streamflow’ or ‘stream_temperature’ for streamflow and stream temperature, respectively.</li> </ul> <p>More details please see README.pdf</p>
Flow Magnetic Tweezers: Gyrase dynamics in absence of drug Ciprofloxacin
<p>The video contains a whole field from a force spectroscopy experiment called Flow Magnetic Tweezers (FMT). It shows E. coli DNA Gyrase manipulating DNA topology by relaxing positive and introducing negative coils in absence of Ciprofloxacin, reaction of Gyrase to external torque and eventual wash with SDS.</p>
Coevolving plasmids drive gene flow and genome plasticity in host-associated intracellular bacteria
<p>Comparative genomics and modeling of plasmids of the obligate host-associated intracellular phylum chlamydiae. </p>
Extreme to phenomenal storm wave impacts on a steep rocky coast, north Mayo, Ireland: video data, image analysis, runup and flow velocity calculations for waves of storms Fionn and Gareth.
<p>The primary data are video (.mp4) files of extreme storm wave impacts on the sites of high elevation (>=20m above high water mark) coastal boulder deposits, recorded during storms Fionn (16/01/2018) and Gareth (12/03/2019), at (54.320355, -9.569633) on the north Mayo coast of Ireland, while the significant wave height was in the range [11m,14m]. There are also .png and .jpg files derived from frames of some of the videos, relating to the analysis of the impacting wave kinematics (runup/landward propagation and flow velocities), together with physical measurements for scale determination and runup/velocity/measurement uncertainty calculations in Excel. The files EventX.mp4 are the primary data for the wave impacts EventX. The files EventX_Frame_Y.jpg are frames sampled from EventX.mp4 at constant time intervals in the temporal vicinity of the impact. The files EventX_Edges_Y.png are the edges derived from the frames with the Canny edge detector. The files EventX_Registration_Y.jpg are the impacting wavefront edges with topographical edges registered on the file ReferenceImage.jpg The files EventX.jpg are the stacked registrations for all Y, from which the impact kinematics are derived. The file Scale_Registration_Position_Velocity_Measurements_AndUncertainty.xlsx contains physical measurements for scale determination, measurements of registration error, and the calculations of impact runup/landward displacement and flow velocities, with their uncertainties. The files JetX_Leacht_a_Chúil.mp4/g are videos of large jet-producing impacts at another site.</p> <p>The files DSCN0066.MP4-DSC0085.MP4 are the raw video observations of Storm Gareth, recorded from 15:35-18:41 UT on 12 March 2019 with a Nikon Coolpix W100, while the significant wave height increased from 12m to in excess of 14m (the timestamp of these videos in Properties->Details->Media Created is one hour later than the UT of creation, because the camera's clock was set to Irish Summer Time). The file GPO15366.MP4 is an example of the GoPro (Hero 5) videos recorded simultaneously.</p>
Multi-fidelity Generative Deep Learning Turbulent Flows
<p>Data sets for the two numerical examples in the paper <a href="https://arxiv.org/abs/2006.04731">Multi-fidelity Generative Deep Learning Turbulent Flows</a> as well as two pre-trained models. In this work, a novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields given the solution of a computationally inexpensive but inaccurate low-fidelity solver. The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation. The deep generative model developed is a conditional invertible neural network, built with normalizing flows, with recurrent LSTM connections that allow for stable training of transient systems with high predictive accuracy. Data is provided from OpenFOAM LES simulations for turbulent flow over backwards step and flow around an array of cylinders.</p> <p>Data-set Files:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_testing.tar.gz?versionId=320a523f-0015-4ba3-8c6e-66733ab5a1af">backward_step_testing.tar.gz</a> - Backward step testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/backward_step_training.tar.gz?versionId=ac34ac15-973d-4fb8-8881-faa17eced69f">backward_step_training.tar.gz</a> - Backward step training data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_testing.tar.gz?versionId=ebbea725-c8b6-4338-978b-dc73f943552e">cylinder_array_testing.tar.gz</a> - Cylinder array testing data.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinder_array_training.tar.gz?versionId=b0409c34-fb19-45c4-bbba-6968fdcfc4d8">cylinder_array_training.tar.gz</a> - Cylinder array training data.</li> </ul> <p>Pre-trained Models:</p> <ul> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/bstepWorkspace400.zip">bstepWorkspace400.zip</a> - Backward step pre-trained model.</li> <li><a href="https://zenodo.org/api/files/cf158661-2a30-4c8f-aa97-cd5c1600910c/cylinderWorkspace400.zip">cylinderWorkspace400.zip</a> - Cylinder array pre-trained model.</li> </ul> <p> </p>
Ionization rate simulation data for "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows"
<p><strong>Background</strong></p> <p>This is a set of 3d data containing ionization rates computed from Hyburn hydrodynamic simulations contained in a Matlab .mat file, along with a plot in both .png and Matlab .fig format, and a Matlab script for plotting.</p> <p>This data is used in figure 5 of the paper "Standing Shock Prevents Propagation of Sparks in Supersonic Explosive Flows".</p> <p>Electric sparks and explosive flows have long been associated with each other. Flowing dust particles originate charge through contact and separate based on inertia, resulting in strong electric fields supporting sparks. These sparks can cause explosions in dusty environments, especially those rich in carbon, such as coal mines and grain elevators. Recent observations of explosive events in nature and decompression experiments indicate that supersonic flows of explosions may alter the electrical discharge process. Shocks may suppress parts of the hierarchy of the discharge phenomena, such as leaders. In our decompression experiments, a shock tube ejects a flow of gas and particles into an expansion chamber. We imaged an illuminated plume from the decompression of a mixture of argon and <100 mg of diamond particles and observe sparks occurring below the sharp boundary of a condensation cloud. We also performed hydrodynamics simulations of the decompression event that provide insight into the conditions supporting the observed behavior. Simulation results agree closely with the experimentally observed Mach disk shock shape and height. This represents direct evidence that the sparks are sculpted by the outflow. The spatial and temporal scale of the sparks transmit an impression of the shock tube flow, a connection that could enable novel instrumentation to diagnose currently inaccessible supersonic granular phenomena.</p> <p><strong>Accessing Data</strong></p> <p>The .mat file can be opened in Matlab to examine data. The 3d arrays contained therein can be viewed in various ways, including using the enclosed script with syntax like plot_isosurfaces(xg,yg,zg,density,max(density(:)),pressure,max(pressure(:))) to produce the included isosurface plot.</p> <p>The data arrays contained are:</p> <p>e: electric field magnitude</p> <p>alpha: ionization rate lengths: ionization lengths (equal to 1/alpha)</p> <p>eOverN: electric field divided by gas number density</p> <p>alphaOverN: ionization rate divided by gas number</p> <p>density density: gas mass density</p> <p>pressure: gas pressure</p> <p>x,y,z: spatial coordinates</p> <p>xg,yg,zg: spatial coordinates in 3d meshgrid format, for Matlab plotting</p> <p>The electric field e was artificially generated from velocities in Hyburn output; alpha was computed from BOLSIG+ with Hyburn input; density and pressure data were from Hyburn.</p> <p> </p>
Example models and queries for Flow Systems Ontology
<p><strong>Example models and queries for Flow Systems Ontology</strong></p> <p>This repository contains the files used to produce the examples in the article manuscript introducing the <a href="https://w3id.org/fso">Flow Systems Ontology (FSO)</a>. This includes both the triples of the example models, and the SPARQL queries used to demonstrate the use cases.</p> <p>The manuscript has been published as "<a href="https://doi.org/10.1016/j.autcon.2021.104067">An ontology to support flow system descriptions from design to operation of buildings</a>" in Automation in Construction.</p> <p>For further details, see the included README.md.</p>
Agricultural land use and livestock composition by case study of the SURE-Farm project - Input data for a dynamic nitrogen flow model
<p>Dataset used as input to the model by Pinsard et al (2021) and results published in D5.5 of the SURE-Farm project.</p>
Dataset of 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks'
<p>Dataset of the article 'Complete flow characterization from snapshot PIV, fast probes and physics-informed neural networks' (https://doi.org/10.1016/j.cma.2023.116652). The codes processing data here are on https://github.com/AlvaroMS90/Complete-flow-characterization-from-snapshot-PIV-fast-probes-and-physics-informed-neural-networks.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085) and by MCIN/AEI /10.13039/501100011033 and the European Union ‘NextGenerationEU/PRTR’ as part of the grant FJC2020-044342-I.</p>
Flow diagram for analysis of high-throughput sequencing data
<p>Tex code and resulting pdf image, summarising the data processing pipeline of high-throughput sequencing data (fastq format files), through mapping the data to a reference genome, and then discovery and genotyping of sequence variants. The latter stage uses both 'GATK Haplotype Caller' for smaller variants, such as single-nucleotide polymorphisms and insertion-deletion polymorphisms, and Genomestrip for variants such as deletions and duplications greater than 1000 nucelotide bases in length. Note that the flow diagram is intended to represent what steps were take in the study, and does not necessarily represent the current optimum methods.</p> <p>The manuscript for which this image is a part of can be found open-access at F1000 Research "Whole genome resequencing of a laboratory-adapted <em>Drosophila melanogaster </em>population sample" https://f1000research.com/articles/5-2644/v1 doi: 10.12688/f1000research.9912.1</p> <p> </p>
Flow_signatures_1366stations
16 flow signatures compiled using daily hydrograph time-series. The choice of flow signatures has been guided by a study by Olden and Poff (2003), which provides recommendations for selection of nine indices describing flow regimes with importance to hydro-ecology. In addition, five flow signatures commonly used in hydrology have been added for comparability (Qsp, CVQ, Q5, Q95, RBFlash) and two variables describing catchment response were calculated (RunoffCo and ActET). The selected flow signatures are: mean specific flow (Qsp), coefficient of variation of daily flow (CVQ), skewness of daily flow (skew), base flow index (BFI), 5th percentile (Q5), high flow discharge (HFD), 95th percentile (Q95), low flow frequency (Lowfr), coefficient of variability of high flow frequency (HighFrVar), coefficient of variability of low flow duration (LowDurVar), mean 30-days maximum (Mean30dMax), constancy of daily flow (const), coeficient of variability in annual number of reversals (RevVar), flashiness of flow (RBFlash), Runoff coefficient (RunoffCo), actual evapotranspiration (ActET). Only gauging stations with at least five whole calendar years of continuous daily data have been selected, and stations with obvious high regulation have been removed. No missing data was allowed over the period and the longest continuous time-series was used at each gauge. This means that time periods differ between gauging stations, the length in year of the used continuous timeseries and the starting year are given in the file (resp. nbYears and startYear). For definitions used for the different flow signatures see reference paper (Kuentz et al., 2016)." This data can be linked to the shapefile "flow_stations_sel1366" available as a separate download. To do so, link the "SUBID_EHYP" filed of the shapefile with the "SUBID" field of this dataset.
Landslides from Space - Mocoa Debris Flow, Columbia (1st April 2017)
<p>More than 330 people died in a rainfall-triggered landslide which occured on 1st April 2017 in Mocoa, Columbia.<br> <br> The pre-event acquisition is from 13th February 2017 (Sentinel-2) and the post-event acquisition is from 4th April 2017 (Sentinel-2). A false colour composite with near-infrared, red and green band is visualised as RGB image.<br> <br> <em>Contains modified Copernicus Sentinel data (2017)</em></p>
Dataset of publication "Investigation of the discharge coefficient in the laminar boundary layer regime of critical flow Venturi nozzles calibrated with different gases including hydrogen"
<p>The attached files contain experimental raw data and fluid properties for the nozzles 1 and 2 mentioned in the paper. The data in the attached files can be used to calculate the Cd values published in the paper.</p>
Development of an Amine Transaminase-Lipase Cascade for Chiral Amide Synthesis under Flow Conditions
<p>The use of multienzymatic systems has gained increasing attention as a method of choice for complex (asymmetric) syntheses. Incompatibilities between substrates, reagents and/or enzymes in one-pot batch conditions can hamper the applicability of a pursued cascade, so the use of flow systems provide useful synthetic solutions. The implementation of immobilised enzymes in continuous flow reactors allows the compartmentalisation and segregation of the enzymes in separate reactors, leading to otherwise disfavoured reaction cascades. Here, an amine transaminase and a lipase have been immobilised on polymer-coated controlled porosity glass carrier materials and studied for the first time together in the transamination of a prochiral ketone followed by acylation of the corresponding chiral amine in flow mode, two incompatible transformations under batch. Thus, the preparation of (<em>R</em>)-<em>N</em>-(1-phenoxypropan-2-yl)acetamide was accomplished after optimisation of the reaction conditions.</p>
Effect of salinity on flows of dense colloidal suspensions - Additional Data
<p>Additional dataset for the article "Effect of salinity on flows of dense colloidal suspensions".</p>
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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)
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.