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Dataset results
4,612 results for “labelling”
Tracking chromatin state changes using nanoscale protoproximity labeling (RNA-Seq)
GEO Series GSE221672. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profiles of human bronchoalveolar epithelial cells (HBEC) transduced with GFP labeled murine focal adhesion kinase (PMX-Purom-GFP-mFAK) or in combination with SUMO ligase Pias1
GEO Series GSE73280. Homo sapiens. 4 samples. Type: Expression profiling by array.
High throughput, label-free isolation of circulating tumor cell clusters in meshed microwells
GEO Series GSE202650. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Harnessing Self-Labeling Enzymes for Selective and Concurrent A-to-I and C-to-U RNA Base Editing
GEO Series GSE160945. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
Effect of 4sU labeling durations on quantification bias in nucleotide conversion RNA-seq data
GEO Series GSE229506. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Global Analysis of mRNA Half-lives Following Biosynthetic Labeling in a Dinoflagellate, Karenia brevis
GEO Series GSE46174. Karenia brevis. 18 samples. Type: Expression profiling by array.
Figure 3 from: Drenska M, Naseva E, Getov IN (2019) The knowledge and experience with the off-label use – results of a survey. Pharmacia 66(4): 217-221. https://doi.org/10.3897/pharmacia.66.e46687
Figure 3 The main off-label medical areas.
Figure 2 from: Drenska M, Naseva E, Getov IN (2019) The knowledge and experience with the off-label use – results of a survey. Pharmacia 66(4): 217-221. https://doi.org/10.3897/pharmacia.66.e46687
Figure 2 The main reasons for off-label use.
Figure 1 from: Drenska M, Naseva E, Getov IN (2019) The knowledge and experience with the off-label use – results of a survey. Pharmacia 66(4): 217-221. https://doi.org/10.3897/pharmacia.66.e46687
Figure 1 Distribution of participants by their medical specialty.
Figure 2 from: Drenska M, Elitova S, Grigorova V, Naseva E, Getov I (2019) Analysis of primary outpatient data for off-label use of medicines in neurology. Pharmacia 66(4): 165-170. https://doi.org/10.3897/pharmacia.66.e36620
Figure 2 Type of off-label use.
Figure 1 from: Drenska M, Elitova S, Grigorova V, Naseva E, Getov I (2019) Analysis of primary outpatient data for off-label use of medicines in neurology. Pharmacia 66(4): 165-170. https://doi.org/10.3897/pharmacia.66.e36620
Figure 1 Off-label use with regard to patients and prescriptions.
Figure 2 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 2 VILE setup showing the camera (a), Stackshot rotary table (b)
Figure 6 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 6 VILE: Image output of VILE after cropping and stitching five individual images together.
Figure 4 from: Dupont S, Humphries J, Butcher AJ, Baker E, Balcells L, Price BW (2020) Ahead of the curve: three approaches to mass digitisation of vials with a focus on label data capture. Research Ideas and Outcomes 6: e53606. https://doi.org/10.3897/rio.6.e53606
Figure 4 Illustrative workflow for the three vial digitisation approaches MALICE, VILE and ReVILE.
Mass spectrometry output SILAC labelled (F/Y) biological replicate 1 - anti-HLA-ABC antibody W6/32
<p>Mass spectrometry output from SILAC labelled (F/Y) ERAP2 WT versus (CRISPR Cas9-mediated) ERAP2-KO lymphoblastoid cell line from a Birdshot Uveitis patient (ERAP1 hap10/10 ERAP2 hapA/A). Peptides were eluted from immuno-purifications from HLA-A29-negative (DK1G8-negatively selected) fractions with anti-HLA-ABC antibody W6/32. The dataset was used for subsequent filtering and differential expression analysis by <em>limma</em>. </p>
HeLa cell images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.
<p>This is a data set that contains <strong>labeled HeLa cell images</strong>, generated in MATLAB® Image Labeler, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background for <strong>deep learning</strong> architecture training. </p> <p>Details of the imaging and preparation have been published in:</p> <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> 2019, <em>5</em>(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> </ul> <p>and</p> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul> <p> </p> <p>Details on the segmentation and analysis of HeLa cells have been published in:</p> <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic Segmentation of HeLa Cells: An Objective Comparison between one Traditional Algorithm and Three Deep-Learning Architectures, BioRxiv, https://doi.org/10.1101/2020.03.05.978478</li> </ul> <p>and</p> <ul> <li> <p>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. <em>J. Imaging</em> 2019, <em>5</em>(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></p> </li> </ul> <p> </p>
Fig. 8 Hydrochus lectotype labels. A – H. obscurus Sharp, 1882 in Taxonomy of Guatemalan water beetles in the genus Hydrochus (Coleoptera: Hydrochidae)
Fig. 8 Hydrochus lectotype labels. A – H. obscurus Sharp, 1882; B – H. debilis Sharp, 1882.
RARE: A Labeled Dataset for Cloud-Native Memory Anomalies
<p>The dataset is linked to the paper RARE: A Labeled Dataset for Cloud-Native Memory Anomalies (<a href="https://doi.org/10.1145/3416505.3423560">https://doi.org/10.1145/3416505.3423560</a>)</p> <p>This dataset has been generated using a microservice for injecting artificial byte stream in order to overload the nodes, provoking memory anomalies,</p> <p>It includes 2 files:</p> <p>- List_of_anomalies.csv includes the details on the anomalies injected</p> <p>- RARE.csv is the actual dataset.</p> <p>There are a total of 30 anomalies injected in the dataset. The full dataset comprise 10K labelled time-series, each with 7062 metrics.</p>
Data from: Video education versus face-to-face education on inhaler technique for patients with well-controlled or partly-controlled asthma: a phase IV, open-label, non-inferiority, multicenter, randomized, controlled trial
Background: Education on inhaler technique is critical for effective asthma treatment. However, traditionally used face-to-face education is a time-consuming, costly, and often laborious. The current study evaluated the efficacy of a newly developed video-based inhaler technique education method. Methods: A total of 184 subjects with well-controlled or partly-controlled asthma were enrolled from 12 hospitals in South Korea from 30 November 2015 to 01 June 2016. Subjects were randomly divided into two groups in a 1:1 ratio; a control group that received face-to-face education, and an study group that received video education. All subjects received fluticasone propionate plus salmeterol xinafoate (Fluterol® 250/50 inhalation capsules) for 12 weeks. The primary outcome measure was forced expiratory volume in the 1st second (FEV1) at 12 weeks. The secondary outcome measures were change in FEV1 at 4 weeks, change in asthma control test (ACT) score, and changes in various inhaler technique parameters. These measures were assessed with a non-inferiority margin of 10% between the control group and the study group. Results: FEV1 was significantly improved at 12 weeks in the control group and the study group. After adjustment, FEV1 improvement was not significantly inferior in the study group compared to the control group. The secondary outcome measures, including change in FEV1 at 4 weeks, ACT score, and various parameters pertaining to inhaler technique and satisfaction at 4 and 12 weeks did differ not significantly in the two groups. In subgroup analysis of elderly subjects and subjects with well-controlled asthma, FEV1 was significantly improved at 12 weeks in the study group but not the control group. Conclusion: The newly developed video education technique investigated functioned as a suitable substitute for face-to-face education on inhaler technique (dry powder inhalation capsule) in patients with stable asthma, particularly in elderly patients and patients with well-controlled asthma.
LLPS REDIFINE allows the biophysical characterization of multicomponent condensates without tags or labels
<p>UPDATE - v3 contains additional jacobianest.m function</p> <p>This zip folder contains the latest NMR data acquired using LLPS REDIFINE method for biomolecular condensates characterization. The Matlab codes are also included.</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.