Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
57
datasets available to search
ShareScore release 0.7.1
Dataset results
57 results for “light scattering”
Intracellular gold nanoparticles influence light scattering and facilitate amplified spontaneous emission generation
<p>This is the raw data concerning publication entitled: Intracellular gold nanoparticles influence light scattering and facilitate amplified spontaneous emission generation.</p> <p>It contains the data to produce graphs and images for the main Figures of the publication.</p>
A Brillouin light scattering study of the spin-wave magnetic field dependence in a magnetic hybrid system made of an artificial spin-ice structure and a film underlayer
<p>We present a combined Brillouin light scattering (BLS) and micromagnetic simulation investigation of the magnetic-field-dependent<br> spin-wave spectra in a hybrid structure made of permalloy (NiFe) artificial spin-ice (ASI) systems, composed of stadium-shaped nanoislands, deposited on the top of an unpatterned permalloy film with a nonmagnetic spacer layer. The thermal spin-wave spectra were recorded by BLS as a function of the magnetic field applied along the symmetry direction of the ASI sample. Magneto-optic Kerr effect magnetometry was used to measure the hysteresis loops in the same orientation as the BLS measurements. The frequency and the intensity of several spin-wave modes detected by BLS were measured as a function of the applied magnetic field. Micromagnetic simulations enabled us to identify the modes in terms of their frequency and spatial symmetry and to extract information about the existence and strength of the dynamic coupling, relevant only to a few modes of a given hybrid system. Using this approach, we suggest a way to understand if the dynamic coupling between ASI and film modes is present or not, with interesting implications for the development of future three-dimensional magnonic applications and devices.</p>
Dynamic Light Scattering for Studying Lens Aging and Cataract Formation
ClinicalTrials.gov study NCT00081991. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Light Scattering Ocular Measurement in the Detection of Dementia
ClinicalTrials.gov study NCT06613971. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Light Scattering Spectroscopy to Determine Brain Tumors
ClinicalTrials.gov study NCT00067418. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Light Scattering Device (DLS) Study of Age-Related Changes in the Lens and Cataracts
ClinicalTrials.gov study NCT00007215. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Dynamic Light Scattering to Study Crystalline Proteins in Young Normal Lenses
ClinicalTrials.gov study NCT00304967. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Dynamic Light Scattering and Keratoscopy for Corneal Examination
ClinicalTrials.gov study NCT00050466. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Pilot Study: Dynamic Light Scattering Device for Studying Early Changes in Cataract
ClinicalTrials.gov study NCT00067483. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Trans-abdominal Fetal Pulse Oximetry; Tissue Light Scattering and Signal Integrity
ClinicalTrials.gov study NCT04876846. IPD Sharing: NO. Countries: 1. Publications: 0.
Trans-abdominal Fetal Pulse Oximetry: Tissue Light Scattering
ClinicalTrials.gov study NCT04081584. IPD Sharing: NO. Countries: 1. Publications: 0.
Light-Scattering Spectroscopy for Detection of Breast Cancer
ClinicalTrials.gov study NCT01755208. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Specialized Molecular Pathways Drive the Formation of Light-Scattering Assemblies in Leucophores
GEO Series GSE280941. Oryzias latipes. 2 samples. Type: Expression profiling by high throughput sequencing.
Oscilloscope measurement signals of the light scattering of individual suspension droplets on a Gaussian beam.
<p>The light scattering of the droplets on a Gaussian beam are measured using a commercial device SpraySpy PL100 from AOM-Systems GmbH. It has one laser source with a wavelength of 405nm along with two detectors placed around and named here as A and B. This laser source as well as the two detectors are pointed towards the droplet chain, which is created by a commercial monodisperse droplet generator from FMP Technology GmbH.</p> <p>The detectors, in the form of photo multipliers, aim to track and capture light scattered from the droplets passed through the Gaussian beam. The light scattering signal is subsequently converted into a voltage signal by a transimpedance amplifier. The measuring signal is digitized by a digital oscilloscope PicoScope 6404B. The signal consists of 32 measurement frames with an individual duration of 20ms sampled by 312.5MS/s. Each frame contains about 1000 individual light scattering signals. Since there are two independent detectors with one signal generated by each, there are also two active channels in the measurement on the oscilloscope named correspondingly Channel A and Channel B.</p> <p>In total, measurements are conducted for a concentration range of 0 to 100%. From 0 to 25% a measurement step amounts to 1\% (1%, 2%...,25%) while from 25 to 100\% a measurement step amounts to 2.5%, rounded up to 3% (25%, 28%, 30%...100%). </p> <p> </p>
Ensemble averaged signals from the light scattering signals of individual suspension droplets.
<p>The ensemble-averaged light scattering signals were constructed from 1000 individual light scattering signals published on https://doi.org/10.5281/zenodo.6614871</p> <p>The light scattering of the droplets on a Gaussian beam are measured using a commercial device SpraySpy PL100 from AOM-Systems GmbH. It has one laser source with a wavelength of 405nm along with two detectors placed around and named here as A and B. This laser source as well as the two detectors are pointed towards the droplet chain, which is created by a commercial monodisperse droplet generator from FMP Technology GmbH.</p> <p>The detectors, in the form of photo multipliers, aim to track and capture light scattered from the droplets passed through the Gaussian beam. The light scattering signal is subsequently converted into a voltage signal by a transimpedance amplifier. The measuring signal is digitized by a digital oscilloscope PicoScope 6404B. The signal consists of 32 measurement frames with an individual duration of 20ms sampled by 312.5MS/s. Each frame contains about 1000 individual light scattering signals. Since there are two independent detectors with one signal generated by each, there are also two active channels in the measurement on the oscilloscope named correspondingly Channel A and Channel B.</p> <p>In total, measurements are conducted for a concentration range of 0 to 100%. From 0 to 25% a measurement step amounts to 1\% (1%, 2%...,25%) while from 25 to 100\% a measurement step amounts to 2.5%, rounded up to 3% (25%, 28%, 30%...100%).</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>
Particle characterization by analyzing light scattering signals with a machine learning approach.
<p>This container includes the measurement data and trained machine learning models associated with the publication: "Particle Characterization by Analyzing Light Scattering Signals Using a Machine Learning Approach."</p> <p>There are three types of data, each marked with a specific prefix:<br>- <strong>Data_</strong><br>- <strong>Pred Data_</strong><br>- <strong>Test Data_</strong></p> <p>The files with the prefix <strong>Data_</strong> contain the data used to train the machine learning model.</p> <p>The files with the prefix <strong>Test Data_</strong> contain the data used to test the machine learning model.</p> <p>The files with the prefix <strong>Pred Data_</strong> contain the results generated after the test data was applied to the machine learning model.</p> <p>Additionally, there are pre-trained machine learning models with the prefix <strong>Model_</strong>.</p> <p>Moreover, a Wolfram Mathematica script is included for training and testing the machine learning models. The script <strong>Script Wolfram Mathematica</strong> is added in <strong>.nb</strong> und in <strong>.pdf</strong> formats.</p> <p><strong>The use of the data is permitted only for academic purposes and not for any commercial purposes.</strong></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.