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ShareScore release 0.9.0
Dataset results
558 results for “Training Data”
Data from: Lower education level is a risk factor for peritonitis and technique failure but not a risk for overall mortality in peritoneal dialysis under comprehensive training system
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Data from: Effect of canine oxytocin receptor gene polymorphism on the successful training of drug detection dogs
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Raw motif mapping bedfile data and model training set class probabilities
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Periodic and heterogeneous solid and velocity data used to train and validate CNN models
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Synthetic and reticulated foam solid and velocity data used to train and validate CNN models
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Autoimmune inflammation causes hematopoietic stem cells to generate a trained immunity program inherited by BMDMs [RNAseq_secondary_data]
GEO Series GSE267569. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
LncRNA data in mice hearts after swimming exercise training or sedentary
GEO Series GSE152948. Mus musculus. 9 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Affymetrix data for training of Endopredict algorithm
GEO Series GSE26971. Homo sapiens. 277 samples. Type: Expression profiling by array; Third-party reanalysis.
Circulating Cell-Free RNA in Blood as a Host Response Biomarker for the Detection of Tuberculosis [training_data]
GEO Series GSE255071. Homo sapiens. 130 samples. Type: Expression profiling by high throughput sequencing.
Training data for "Clustering 3K PBMCs with Scanpy"
<p>Single-cell RNA-seq analysis is a rapidly evolving field at the forefront of transcriptomic research, used in high-throughput developmental studies and rare transcript studies to examine cell heterogeneity within a populations of cells. The cellular resolution and genome wide scope make it possible to draw new conclusions that are not otherwise possible with bulk RNA-seq.</p> <p>In this tutorial, we will investigate clustering of single-cell data from 10x Genomics, including preprocessing, clustering and the identification of cell types via known marker genes, using <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> (<a href="http://0.0.0.0:4000/training-material/topics/transcriptomics/tutorials/scrna-scanpy-pbmc3k/tutorial.html#wolf2018scanpy">Wolf <em>et al.</em> 2018</a>). It is illustrated using a dataset of Peripheral Blood Mononuclear Cells (PBMC), extracted from a heal, freely available from 10X Genomics. The dataset contains 2,700 single cells sequencd using Illumina NextSeq 500. The raw sequences have been processed by <a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger"><strong>cellranger</strong></a> pipeline from 10X to extract an unique molecular identified (UMI) count matrix.</p> <p> </p>
Wlutz TensorFlow Training Data
<p>Wlutz TensorFlow Training Data</p>
'Learning the production cross sections of the Inert Doublet Model' training data set.
<p>Training data set used in the ''Learning the production cross sections of the Inert Doublet Model'' subproject, made of 50000 samples with 5 input values (MH0, MA0, MHC, lam2, lamL) and 8 target values (xsec_3535_13TeV, xsec_3636_13TeV, xsec_3737_13TeV, xsec_3537_13TeV, xsec_3637_13TeV, xsec_3735_13TeV, xsec_3736_13TeV, xsec_3536_13TeV) from a parameter space of the Inert Doublet Model chosen as: 50< MH0, MA0, MHC<3000GeV;−2π < lam2,lamL<2π. The cross sections were computed at leading order using MADGRAPH2.6.4 and the IDM UFO implementation from the FeynRules data base.</p> <p> </p> <p> </p>
Learn2Reg Challenge: CT Lung Registration - Training Data
<p>For more information about this dataset go to: https://learn2reg.grand-challenge.org/</p>
Training data for "RNA-Seq analysis with AskOmics Interactive Tool"
<p>Additional files for Rna-Seq analysis with AskOmics IT.</p>
Data from: A structured training program for health workers in intravenous treatment with fluids and antibiotics in nursing homes: a modified stepped-wedge cluster-randomised trial to reduce hospital admissions
Objectives: Hospitalization is potentially detrimental to nursing home patients and resource demanding for the specialist health care. This study assessed if a brief training program in administrating intravenous fluids and antibiotics in nursing homes could reduce hospital transfers and ensure high quality care locally. Design: A pragmatic and modified cluster randomized stepped-wedge trial with randomization on nursing home level. Participants: 330 cases in 296 nursing home residents from 30 nursing homes were included. Cases were patients provided intravenous antibiotics or intravenous fluids, in nursing home or hospital. Primary outcome was localization of treatment, secondary outcomes were number of days treated, days of hospitalization among admitted patients, type of antibiotics used and 30-day mortality. Intervention: The nursing homes sequentially received a one-day educational program for the health workers including theory and practical training in intravenous treatment of dehydration and infection, run by two skilled nurses. After completing the training program, the nursing homes had competence to provide intravenous treatment locally. Results: The intervention had a highly significant effect on treatment in nursing homes (OR 8.35, 2.08 to 33.6; P<0.01, or RR 2.23, 1.48 to 2.56). The number treated in nursing homes was stable over time; the number treated in hospital gradually decreased (chi square for trend P< 0.001). Among patients receiving intravenous antibiotics in the nursing homes, 50 (46%) died within 30 days, compared to 30 (36%) treated in the hospital (P=0.19). Among patients receiving intravenous fluids locally, 21 (19%) died within 30 days, compared to 2 (8%) in the hospital group (P=0.34). Mortality was associated with reduced consciousness and elevated c-reactive protein. Conclusions: A brief educational program delivered to nursing home personnel was feasible and effective in reducing acute hospital admissions from nursing homes for treatment of dehydration and infections.
Data from: Effects of a systematically offered social and preventive medicine consultation on training and health attitudes of young people not in employment, education or training (NEETs): an interventional study in France
Background NEETs (young people not in employment, education or training) are at higher risk for poorer mental and physical health. In France, the Missions locales (MLs) are the only social structures dedicated to this population. We sought to determine whether the systematic offer of a social and preventive medicine consultation at a ML might increase NEET participants' access to training in the 12 months following the intervention. Methods This intervention research was a parallel randomised controlled interventional study conducted at five MLs in mainland France in 2011-2012. It included 976 NEETs aged 18 to 25 years who attended one of the five MLs. At inclusion, participants were randomly assigned (1:1:1) to three groups: those in the first group were invited to see a social worker (not studied in this paper), those in the second group were invited to see a doctor and a social worker (intervention group), and the third was a control group. The primary outcome was participation in at least one training session during the year following study inclusion. Results Among the 976 participants, 504 were randomly assigned to the intervention group and 472 to the control group; 704 (72.1%) were included in the analyses. A significantly higher proportion of the participants in the intervention group participated in a training session in the 12 months following the intervention than of those in the control group (63.3% vs 55.6%; p=0.04). This difference was significantly greater for women, those less than 21 years of age, those unstably housed and those with a lower level of education. Conclusions Social and preventive medicine consultations that are fully integrated into the social services for NEETs have an impact on their access to training and contribute to changing some of their health-related behaviours. This may improve their access to the labour market.
Random Forest Cloud Model for Predicting Liquid Cloud Microphysical Properties from A-Train Data
<p>Code for creating and analyzing the performance of a random forest model to predict cloud optical depth and cloud top effective radius from A-train satellite observations. Because of storage limitations, this directory does not include full satellite dataset, but the CloudSat data is available from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/) and the CALIPSO data from NASA's Atmospheric Science Data Center (https://asdc.larc.nasa.gov/project/CALIPSO). </p>
Data about "The individual training history shapes soccer players' ability to predict teammates' and opponents' moves"
<p>This repository contains the data related to the two experiments presented in the article: <em>"The individual training history shapes soccer players' ability to predict teammates' and opponents' moves."</em> In addition to the accuracy scores of individual players, data on the soccer experience of the participants are also provided. Finally, the videos used as stimuli in the experimental procedure are included. The videos depict a soccer player performing typical actions for different positions on the field, which are stopped before the completion of the movement.</p>
DeepPrecip Training Data
<p>DeepPrecip model training data (MRR, Pluvio, surface meteorology and ERA5 2mt and wind velocity data) organized by observation site.</p>
Data from: Education research: simulation training for neurology residents on acquiring tPA consent: an educational initiative
[No abstract entered]
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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.