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71
datasets available to search
ShareScore release 0.9.0
Dataset results
71 results for “dynamic lighting”
Glaucoma Screening Using Dynamic Analysis of Computerized Pupillary Light Reflex Assessment Device
ClinicalTrials.gov study NCT04595227. IPD Sharing: NO. Countries: 1. Publications: 0.
Light in Frail Elderly - the Effect of a Dynamic Light for Sleep and Circadian Rhythm
ClinicalTrials.gov study NCT05107947. IPD Sharing: NO. Countries: 1. Publications: 0.
Effect of Dynamic Light Application on Performance of ICU Nurses
ClinicalTrials.gov study NCT01831167. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Light-Activatable TET-Dioxygenases Reveal Dynamics of 5-Methylcytosine Oxidation and Transcriptome Reorganization
GEO Series GSE147917. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Data from: Phylogeographic diversification and postglacial range dynamics shed light on the conservation of the kelp Saccharina japonica
Open the record for dataset details and reuse information.
Local light signalling at the leaf tip drives remote differential petiole growth through auxin-gibberellin dynamics
GEO Series GSE218944. Arabidopsis thaliana. 204 samples. Type: Expression profiling by high throughput sequencing.
Temporal analysis of physiological and transcriptome responses during high light exposure that lead to dynamic acclimation in Arabidopsis
GEO Series GSE78251. Arabidopsis thaliana. 100 samples. Type: Expression profiling by array.
Supporting data for "Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing"
<p>Supporting data for the manuscript "Health benefits of US light-duty vehicle electrification: roles of fleet dynamics, clean electricity, and policy timing". Contains the files generated by FLAME-AQ for the scenarios included in the publication.</p> <p>The FLAME-AQ model is available here: https://github.com/jean-schmitt/FLAME-AQ</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>
Uncovering the transcriptional molecular dynamics of shelf life extension and system acquired resistance induction to Fusarium pallidoroseum in melon fruits by the use of pulsed-light
GEO Series GSE256527. Cucumis melo. 18 samples. Type: Expression profiling by high throughput sequencing.
A dynamic transcriptome underling the light inhibition of mesocotyl elongation in rice seedling
GEO Series GSE136318. Oryza sativa. 6 samples. Type: Expression profiling by high throughput sequencing.
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.