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139
datasets available to search
ShareScore release 0.9.0
Dataset results
139 results for “dynamic signals”
CD5 dynamically calibrates basal NF-KB signaling in T cells during thymic development and peripheral activation
GEO Series GSE151395. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [control]
GEO Series GSE203619. Gallus gallus. 21 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [lens induced]
GEO Series GSE203629. Gallus gallus. 21 samples. Type: Expression profiling by high throughput sequencing.
Temporal dynamics of signaling and transcription underlying early adaptation to oncogenic BRAF inhibition in melanoma
GEO Series GSE252781. Homo sapiens. 13 samples. Type: Expression profiling by high throughput sequencing.
snRNA-seq dataset for "Morphine regulates astrocyte transcriptional dynamics by stimulation of glucocorticoid signaling"
GEO Series GSE308304. Rattus norvegicus. 14 samples. Type: Expression profiling by high throughput sequencing.
Systematic Discovery of Hidden Dynamics and Regulatory Codes in MAPK Signaling Networks
GEO Series GSE125906. Arabidopsis thaliana. 141 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [1d]
GEO Series GSE203555. Gallus gallus. 14 samples. Type: Expression profiling by high throughput sequencing.
Cell fate and adhesion dynamics rely on Ror2-mediated alternative Wnt signaling during tumor progression
GEO Series GSE93815. Mus musculus. 26 samples. Type: Expression profiling by array.
Antigen perception in T cells by long-term Erk and NFAT signaling dynamics [scRNAseq]
GEO Series GSE242494. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Antigen perception in T cells by long-term Erk and NFAT signaling dynamics [RNAseq]
GEO Series GSE242418. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
b-catenin Signaling Dynamics Regulate Cell Fate in Differentiating Neural Stem Cells
GEO Series GSE155862. Rattus norvegicus. 20 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus [6d]
GEO Series GSE203604. Gallus gallus. 14 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>
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>
Interaction dynamics determine signaling and output pathway responses
GEO Series GSE94495. Saccharomyces cerevisiae. 24 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of gene regulatory networks in chickens reveals dynamic temporal changes in retinal signaling cascade underlying compensation to lens-imposed optical defocus
GEO Series GSE203630. Gallus gallus. 84 samples. Type: Expression profiling by high throughput sequencing.
Combined multiphoton live-cell imaging and molecular systems approaches reveal mosaic contribution of dynamic transiently signaling INPs to the adult Dentate Gyrus NSC niche
GEO Series GSE50148. Mus musculus. 11 samples. Type: Expression profiling by array.
Conserved and dynamic ESR1 signaling in developing cortex mediates early postnatal social behavior [snATAC-seq]
GEO Series GSE277917. Mus musculus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Conserved and dynamic ESR1 signaling in developing cortex mediates early postnatal social behavior [scRNA-seq]
GEO Series GSE277918. Mus musculus. 2 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.