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111
datasets available to search
ShareScore release 0.9.0
Dataset results
111 results for “flow dynamics”
Parenchymal border macrophages regulate CSF flow dynamics [leptomeningeal macs]
GEO Series GSE188284. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Parenchymal border macrophages regulate CSF flow dynamics [brain]
GEO Series GSE188283. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Suppelementary files for the article "A visualisation of flow dynamics in tide dominated unconfined coastal aquifers by using physical experiments"
Open the record for dataset details and reuse information.
Influences of deposition upslope the barrier on the dynamic impact of dry granular flow
<p>The data used in this paper can be available in the uploaded file.</p>
Dataset for Role of Initial Particle Deposition in Dataset for Collapse Dynamics and Deposition Morphology of Submarine Granular Flows using CFD-DEM Coupling Method
Open the record for dataset details and reuse information.
Dynamic MRI and Quantitative MR CSF Flow Studies in Craniovertebral Junction Anomalies
ClinicalTrials.gov study NCT00795080. IPD Sharing: NO. Countries: 0. Publications: 0.
Myocardial Flow Reserve and 99mTc-DTPA Cardiac Dynamic SPECT
ClinicalTrials.gov study NCT02844686. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Parenchymal border macrophages regulate CSF flow dynamics [CLO depletion]
GEO Series GSE206115. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
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>
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>
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>
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.