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Dataset results
369 results for “droplet”
Identification of host regulators of Mycobacterium tuberculosis phenotypic states uncovers a role for the MMGT1-GPR156-lipid droplet axis in the induction of persistence [AR_SNR_RNAseq]
GEO Series GSE210687. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
The contribution of lipid droplets formation-related genes during adipogenesis to osteoblastogenesis
GEO Series GSE175624. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Alarmin-loaded extracellular lipid droplets induce airway neutrophil infiltration during type 2 inflammation
GEO Series GSE245964. Mus musculus. 16 samples. Type: Expression profiling by high throughput sequencing; Other.
Lipid droplet-mediated danger signaling balances Trem2+ macrophage induction and NLRP3 inflammasome activation in NASH
GEO Series GSE230700. Mus musculus. 14 samples. Type: Expression profiling by high throughput sequencing.
PPARa regulates ER-lipid droplet protein Calsyntenin-3b to promote ketogenesis in hepatocytes
GEO Series GSE289761. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Effects on transcriptional regulation and lipid droplet characteristics in the liver of female juvenile pigs after early postnatal feed restriction and refeeding are dependent on birth weight
GEO Series GSE43826. Sus scrofa. 30 samples. Type: Expression profiling by array.
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>
Water-Sensitive Paper Droplet Annotation
<p>The provided dataset consists of:</p> <ul> <li>300 synthetic images featuring water-sensitive paper ()</li> <li>2 real water-sensitive paper images manually annotated</li> <li>127 real water-sensitive paper images, divided into squares, pre-annotated</li> </ul> <p>The synthetic dataset was generated using an automated algorithm that creates individual droplets and positions them against a yellow artificial background. Annotations for instance segmentation each droplet are stored in a text file formatted according to YOLOv8 annotation format.</p> <p>Some key features include:</p> <ul> <li><strong>Distribution of number of droplets per image:</strong> the number of droplets per image is determined<br>based on the configuration values to follow a normal distribution.</li> <li><strong>Size distribution of droplets: </strong>the algorithm calculates the size of each droplet based on the<br>Rosin-Rammler distribution.</li> <li><strong>Image Resolution:</strong> images were created with three different resolutions</li> <li><strong>Yellow Background:</strong> the background of each image is composed by a yellow radial gradient generated for each water-sensitive paperimage. The gradient transitions between two randomly chosen tones of yellow from a list of shades of yellow taken from real images of water-sensitive paper. </li> <li><strong>Droplet Color: </strong>the colors of the droplets are taken from two distinct real datasets of water-sensitive paper.</li> <li><strong>Droplet Shape: </strong>the shapes of the droplets are selected from a list containing 25 404 shapes, which are taken from real water-sensitive paper images.</li> </ul> <p>Each set of images of this dataset is organized into two folder:</p> <ol> <li><strong>image: </strong>contains the water-sensitive paper images</li> <li><strong>label: </strong>contains the labels in YOLOv8 polygon format of each one of the droplets in the image</li> </ol>
Host responses that correlate with respiratory droplet transmission of swine-origin influenza viruses in ferrets.
GEO Series GSE79282. Mustela putorius furo. 130 samples. Type: Expression profiling by array.
Droplet based single cell RNA sequencing profiles of a murine model of type-1 diabetes
GEO Series GSE111752. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Chromatin accessibility analysis in lipid droplet-laden macrophages with or without SLC27A1 silencing (ATAC-seq)
GEO Series GSE313205. Mus musculus. 6 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
MobiChIP: a compatible library construction method of single-cell ChIP-seq based droplets
GEO Series GSE273350. Homo sapiens; Mus musculus; Cricetulus griseus. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Droplet-based scRNA-seq of plasma-positive microglia and plasma-negative microglia from hypothalamus region in young mice
GEO Series GSE264113. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
The lipid droplet protein DHRS3 is a regulator of melanoma cell state
GEO Series GSE262095. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Lipid droplets impair anti-tumor immunity by disrupting IFNGR1 trafficking via TGN diacylglycerol depletion
GEO Series GSE289818. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in picolitre-sized droplets
GEO Series GSE174696. Homo sapiens. 1564 samples. Type: Expression profiling by high throughput sequencing.
Lipid Droplet-Enriched Luminogens Enable Adoptive Macrophage Transfer for Treatment of Bacterial Sepsis
GEO Series GSE289543. Staphylococcus aureus. 6 samples. Type: Expression profiling by high throughput sequencing.
NCBP2 promotes colorectal cancer growth and metastasis by driving lipid droplet accumulation by stabilizing LIPG mRNA
GEO Series GSE270402. Homo sapiens. 4 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.