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5,371 results for “Flow”
Dataset for "Characterization of stream, hyperconcentrated and debris flows from seismic signal: Insights into sediment transport mechanisms and flow dynamics"
<p>Yang et al. (2023) Dataset for "Characterization of stream, hyperconcentrated and debris flows from seismic signal: Insights into sediment transport mechanisms and flow dynamics", Journal of Geophysical Research-Earth Surface</p>
Reconstruction software: 4D imaging of two-phase flow in porous media using laboratory-based micro-Computed Tomography
<p>This repository contains the dataset and the computed tomography (CT) reconstruction software used in the journal article "4D imaging of two-phase flow in porous media using laboratory-based micro-Computed Tomography". </p>
Numerical Modeling for Choked Flow of CO2 through Converging-Diverging Fracture
<p>These ANSYS archive files were made to investigate the choked flow of CO2 through the converging-diverging (CD) fractures. Here, Aungier-Redlich-Kwong Equation of State is used as an equation of state. The archive files contain the geometry, mesh of each CD fracture and numerical settings properties for calculating. Note that the archive files were created with ANSYS 2020 R1 version. Please see the organizational document file named 'Zenodo Information_Yang and Han.docx'. </p>
Dataset related to article "Hemodynamics in AVF over time: A protective role of vascular remodeling toward flow stabilization"
<p>Dataset containing magnetic resonance images, blood flow boundary conditions and post-processing of computational fluid simulations results</p>
Model codes and data for ``Low-cost High-Speed Photogrammetry for Measuring Dynamic Flow Deposits"
<p>The dataset contains the raw and processed data of the experiment, and the Matlab code is used to synchronize the cameras. The content includes: </p> <p><br>1. CatchFlashlights.m: Matlab algorithm to identify flashlight time steps for camera synchronization.<br>2. cameras_01_10.rar, cameras_11_20.rar, ..., cameras_61_70.rar: videos for the experiment, including calibration, flashlights, and fan evolution for the 70 cameras.<br>3. targets.txt: xyz data for 100 reference points.<br>4. DEM_tif_600_699.rar, DEM_tif_700_799.rar, and DEM_tif_800_900.rar: DEMs for every second during 600-900 seconds for the debris flow fan experiment described in the manuscript.</p>
Simulation of Wind Flow Field in the Main Campus of Dhaka University of Engineering & Technology
<p>This is data file research project title "Simulation of Wind Flow Field in the Main Campus of Dhaka University of Engineering & Technology"</p>
Flow with moving tool-joint
Open the record for dataset details and reuse information.
Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach
<p>This container includes the measurement data, python script and weights of trained machine learning model associated with the scientific work, which will be presented in 2025 at the <em><strong>Turbulence, Heat and Mass Transfer 11</strong> </em>conference in Tokyo.</p> <p><strong>Title:</strong> Refractive Index Determination of Dynamic Droplets in Flow by Analyzing Light Scattering Signals with a Machine Learning Approach <br><strong>Authors:</strong> W. Schaefer<br><strong>Affiliation:</strong> ai-quanton GmbH, Dr.-Werner-Freyberg-Str. 7, 69514 Laudenbach, Germany <br><strong>Contact:</strong> info@ai-quanton.com </p> <p>The following data files are provided:</p> <ul> <li><strong>Dataset_40_4ch1234.rar (unpacked: Dataset_40_4ch1234.pth)</strong></li> <li><strong>M1_SegmentsTHR40.csv</strong></li> <li><strong>SegmentsTHR40.rar (unpacked: M1_SegmentsTHR40.csv ... M55_SegmentsTHR40.csv)</strong></li> <li><strong>Model_weights_4ch1234.pth</strong></li> </ul> <p> </p> <p><strong>Dataset_40_4ch1234.pth</strong> is a file, containing a ready-to-use dataset of 4-channel signals prepared for use in Python scripts.</p> <p><strong>M1_SegmentsTHR40.csv </strong>is an example of a file used for storing and loading light scattering signals of individual droplets with corresponding additional data. The meaning of each column is:</p> <p>'MID' – measurement ID</p> <p>'FID' – frame ID</p> <p>'SID' – signal ID</p> <p>'CID' – channel ID</p> <p>'NOP' – number of parts</p> <p>'PNM' – part number</p> <p>'TCH' – trigger channel</p> <p>'TLE' – trigger level</p> <p>'TID' – trigger ID</p> <p>'CON' – label used for training</p> <p><strong>SegmentsTHR40.rar</strong> is an archived folder containing .csv files, the same format as M1_SegmentsTHR40.csv.</p> <p><strong>Model_weights_4ch1234.pth </strong>contains weights for a model trained on data from all 4 channels.</p> <p> </p> <p><strong>External files:</strong></p> <p>The correcponding repository to this dataset is published on Azure Dev Ops: <a href="https://dev.azure.com/ai-quanton/PBa202">https://dev.azure.com/ai-quanton/PBa202</a><br>This repository contains the Python script developed for a neural network that determines the refractive index of single droplets by analyzing light scattering signals generated as they pass through a Gaussian beam. </p> <p>The script is designed to build and test a machine learning model capable of accurately predicting refractive indices from light scattering data in dynamic spray environments.</p>
Chapter 5 - CO2 electrolysis under pressure pulsed flow
<p>This file contains the datasets relating to Chapter 5 of the PhD thesis "Mass transport at gas evolving Electrodes" by Jorrit Bleeker.</p> <div>This chapter has also been submitted as a publication titled</div> <div>"Pressure-pulsed flow triples mass transport in aqueous CO2 electrolysis"</div> <div> </div> <div>Authors:</div> <div>Jorrit Bleeker1, Lisanne C. Bakker1, Sue S.J. van Deursen1, Timo J.J.M. van Overveld1, Katie M.R. Lawrence2, </div> <div>Isabell Bagemihl1, Giacomo Lastrucci1, Duco Bosma1, Christiaan V. Schinkel1, Evert C. Wagner1, </div> <div>J. Ruud van Ommen1, David A. Vermaas1,*</div> <div> </div> <div>1Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, the Netherlands</div> <div>2Process & Energy Department, Faculty of Mechanical Engineering, Delft University of Technology, Leeghwaterstraat 39, Delft 2628 CB, the Netherlands</div> <div>*Corresponding Author, email: D.A.Vermaas@tudelft.nl </div> <div> </div> <div>Instruction:</div> <div> </div> <div>1. The data for all figures are included in excel files here.</div> <div> </div> <div>2. For Figure S11 the python scripts and accompanying datafiles are included:</div> <div>We recommend running these in Spyder 4.2.5. (Python 3.8)</div> <div> </div> <div>3. Supplementary videos are included as MP4 files here, we recommend playing them on VLC media player</div>
Flow survey data, OAL-Italy Panaro
<p>The flow survey of OAL-Italy Panaro river (lon 11.05°, lat 44.73°) was carried out on 29 April 2022.</p> <p>Two locations were chosen for the survey:</p> <ul> <li>the first one, where Nature Based Solutions were installed in the framework of OPERANDUM project and where ADV data (.csv format) were collected at three different points;</li> <li>the second one, upstream the first one, close to the town bridge, that allowed ADCP measurements (.nc format) in order to estimate river discharge at that section.</li> </ul> <p> </p>
Material extraction (used) (construction minerals) from Global Material Flows Database
<p>The following data was used in the paper "Schiller & Roscher (2023). Impact of Urbanization on construction material consumption: A global analysis" to calculate material consumption of non-metallic mineral construction materials.</p> <p>This dataset provided data on the extraction of mineral construction materials that are used for further processes. The data represent the extraction of raw materials from the environment by country. They are based on reported data on the one hand and estimated data on the other. The specific assumptions and factors for the estimates can be found in Krausmann et al. (2009). Growth in global material use, GDP and population during the 20th century. Ecological Economics 68(10) 2696-2705. doi: 10.1016/j.ecolecon.2009.05.007.<br> In the MFA methodology, some categories of materials are defined as "unused extraction" because they are not economically used or further processed. For example, unused materials include overburden from mining activities and unused residues from biomass extraction (OECD, 2008). These are not included in presented data.</p> <p>The following materials belong to the group of construction minerals: asphalt, chert and flint, common clay, clay for bricks etc., crushed stone, igneous rock, lava sand, limestone, marl, shell, loam, marble, travertines, sand and gravel, sandstone, slate and turfaceous rock (see Lutter, S., Lieber, M., & Giljum, S. (2016). Global Materialflow database. Material extraction data. Technical Report, Version 2015.1. retrieved February 2018 from www.materialflows.net).<br> The status of the given data is spring 2018 and was downloaded from www.materialflows.net at this time. Due to restructuring of the website, 2018 data is no longer available online. Current data on material extraction and consumption can be found on United Nations Environment Programme, International Resource Panel (IRP). (2023). Global Material Flows Database. https://www.resourcepanel.org/global-material-flows-database.</p>
Ambulatory Blood Pressure Measurement in Children With Congenital Urine Flow Obstruction
ClinicalTrials.gov study NCT00764543. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Sources of Variability in Peak Expiratory Flow
ClinicalTrials.gov study NCT00005382. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Role of Endothelin-1 Modulating Insulin-stimulated Blood Flow and Sympathetic Nervous System Activity in Obesity
ClinicalTrials.gov study NCT05518422. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Insulin Action: Role of Skeletal Muscle and Insulin-Mediated Blood Flow
ClinicalTrials.gov study NCT00018304. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Activation of Epidermal Growth Factor Receptor Pathway in Slow-Flow Vascular Malformations
GEO Series GSE130807. Homo sapiens. 11 samples. Type: Expression profiling by high throughput sequencing.
Construction of multilayered small intestine-like tissue by reproducing interstitial flow
GEO Series GSE244013. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Expression analysis in anaerobic Hanford 100H aquifer flow-through columns receiving lactate and chromate
GEO Series GSE56554. mixed sample. 2 samples. Type: Expression profiling by array.
Collaborative Testing of Turbulence Models: More Recent Compressible Flow Cases
This grouping contains more recent compressible-flow cases.
Collaborative Testing of Turbulence Models: Other Flow Cases
This grouping contains more recent compressible-flow cases.
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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.