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1,855 results for “Framework”
Data for "A unified model-based framework for doublet or multiplet detection in single-cell multiomics data"
<p>This repository contains all the data necessary for replicating the COMPOSITE multiplet detection results featured in our manuscript, 'A Unified Model-Based Framework for Doublet or Multiplet Detection in Single-Cell Multiomics Data'. The data are ready to be directly used as input for the COMPOSITE cloud-based application or the Python package 'sccomposite' to replicate the results.</p>
Literature Review for the work "Understanding the Outcomes of Customer-Facing In-Store Technology: A Structured Literature Review and Integrative Framework"
<p>Dataset for the structured literature proposed in the work titled "Understanding the Outcomes of Customer-Facing In-Store Technology: A Structured Literature Review and Integrative Framework" (unpublished). </p>
Green Transition Monitoring Framework for WB (first draft)
<p><span>This dataset comprises a set of indicators for measuring or assessing green transition in Western Balkan countries, based on the research cases developed through the GreenFORCE project. The indicators will be validated with local stakeholders and re-published as a Report on Monitoring Green Transition in WB.</span></p>
FIGURE 1 in Unprecedented interweaving of single-helical and unequal double-helical chains into chiral metal–organic open frameworks with multiwalled tubular structures
FIGURE 1. Stipesoculus productus sp. nov. ♂, Holotype. Habitus. Scale bar = 1 mm.
Supplementary document of "A Conceptual Cognitive Framework for Tinnitus"
<p>TinnitusCCF_Analyses.zip includes the SPSS analysis for all sub-datasets.</p> <p>TinnitusCCF_Datasets.zip includes three datasets in the format of sav:</p> <ul> <li>Tinnitus_CCF_FullDataset</li> <li>Tinnitus_CCF_ClinicalDataset</li> <li>Tinnitus_CCF_NeutralDataset</li> </ul> <p>Tinnitus_CCF_SyntaxCode.zip includes the syntax in the SPSS platform.</p> <p><strong>Funding</strong></p> <p>This research is part of a Multidisciplinary Cognitive Rehabilitation (MCR) Platform and was supported (Grant number: 2013/07375-0) by the Innovation and Diffusion of Mathematical Sciences Center Applied to Industry (CEPID-CeMEAI) of Sao Paulo Research Foundation (FAPESP), the University of Sao Paulo, and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) for student’s scholarship.</p> <p> </p> <p><strong>Ethical approval and consent to participate</strong></p> <p>Data were collected from participants of two running studies on neurofunctional tinnitus model validation - 1) A randomized crossover three-session double-blind study and 2) An observational prospective cohort study, both approved by the Ethics Committee for Analysis of Research Projects, Specialized Center of Otorhinolaryngology and Speech Therapy, Hospital das Clínicas de Ribeirão Preto, University of São Paulo, Brazil (HCRP no 55716616.1.1001.5440, and HCRP no 09813519.1.0000.5440; internationally registered with U1111-1236-5441). All subjects gave written informed consent.</p>
Database of NGS results of GHRL gene of intensive fattening lambs of sheep breeds raised in Latvia in 2022 within the framework of project of the Latvian Council of Science LZP-2021/1-0489 project
<p>Project of The Latvian Council of Science (LCS) - LZP-2021/1-0489 project: “<strong>Development of an innovative approach to identify biological determinants involved in the between-animal variation in feed efficiency in sheep farming</strong>.”</p> <p><strong>The aim of the project</strong> is to determine whether the feed efficiency status of Latvian meat sheep breeds could be predicted using a panel of genetic and molecular markers previously found to be associated with divergent feed efficiency status in a training population of lambs when fed the same diet.</p> <p><strong>Novelty</strong>: to determine the parameters predicting the most productive result of lamb rearing, we set out to develop the cheapest and most effective method for determining markers of feed efficiency - based on molecular and genetic markers obtained from the blood of live lambs.</p> <p><strong>About the project:</strong></p> <p>The costs associated with lambing (buying or keeping sheep) and preparing or purchasing feed are the two largest components of variable costs in sheep raising. Feed costs are high due to poor grain growing conditions in major producing countries, the use of feed grains in ethanol production, and increased competition for land in crop production compared to urban development. Feed efficiency in growing lambs (i.e., the animal's ability to reach a market or adult body weight (BW) with the least amount of feed intake) is a key factor in the sheep industry. Improving feed efficiency reduces production costs. Improving Feed efficiency by 5% can bring economic benefits that are up to four times higher than a 5% increase in average daily gain (ADG).</p> <p>Traditionally, meat breeding programs have focused on outputs due mainly to the routine availability of phenotypic data on outputs or correlated traits. Currently, no marker has successfully explained enough of the variability in feed efficiency that they were used as part of a routine improvement program, as well as according to our data, no genetic parameters for performance and feed efficiency traits are available for sheep. The physiological determinants of feed efficiency or putative biomarkers used to analyze animal-to-animal variation in live lambs could be used as a cost-effective and rapid tool for genetic selection or management decisions.</p> <p>As with other complex traits (also referred to as quantitative or polygenic traits), Feed efficiency is controlled by many genes which are involved in several molecular mechanisms and pathways causing animal-to-animal genetic variation in different biological functions. GWAS results suggested genes impacting lipid and energy metabolism as markers of Feed efficiency. Detection of many variants in these genes is necessary to predict or identify efficient animals accurately.</p> <p>The Ghrelin (GHRL) hormone's main role is regulating feed intake, body weight and gastrointestinal motility. It stimulates appetite by acting on the hypothalamic arcuate nucleus. Ghrelin is orexigenic that transmits a hunger signal from the periphery to the central nervous system and is involved in mealtime hunger and meal initiation.</p> <p><strong>About the data of the project:</strong></p> <p>LZP-2021/1-0489 project data on lamb samples of the year 2022, or the A22 group, which consists of 76 intensively fattened lambs from six breeds.</p> <p>Full GHRL genes (5 exons) were sequenced using Illumina NGS technology (AmpliSeq). The GHRL gene has been sequenced in lambs of Latvia (in sheep research in Latvia) for the first time.</p> <p>Information about 106 loci was found comparing with the last reference sequences ARS_UI_Ramb_v2.0. Forty (53) loci form 11 groups with complete linkage disequilibrium between polymorphisms. Seventeen (17) new SNP (without ID number) were found.</p> <p>The database contains information on each locus for each lamb and the distribution of alleles and genotypes for each locus across all 76 samples. The data will be used in (1) analysis of Latvian sheep and (2) analysis of association with feed efficiency.</p> <p> </p> <p>The data is the<strong> joint property</strong> of the participants of the LCS project: the University of Latvia and the Latvian University of Life Sciences and Technologies.</p>
Replication Package - The applicability of a hybrid framework for automated phishing detection
<p>Replication package and dataset for the paper: The applicability of a hybrid framework for automated phishing detection</p>
A deep learning framework for the reconstruction of gene expression profiles derived from formalin-fixed paraffin-embedded samples
GEO Series GSE298630. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Bioplatforms Australia: Antibiotic Resistant Sepsis Pathogens Framework Initiative - Streptococcus pneumoniae 4496
GEO Series GSE152828. Streptococcus pneumoniae. 18 samples. Type: Expression profiling by high throughput sequencing.
Refined cellular activity expression signatures provide a targeted framework to quantify phenotypic intra-tumour heterogeneity in single-cell data I
GEO Series GSE310219. Homo sapiens. 3 samples. Type: Expression profiling by high throughput sequencing.
Refined cellular activity expression signatures provide a targeted framework to quantify phenotypic intra-tumour heterogeneity in single-cell data
GEO Series GSE310225. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Bioplatforms Australia: Antibiotic Resistant Sepsis Pathogens Framework Initiative - Streptococcus pneumoniae 180/15
GEO Series GSE152832. Streptococcus pneumoniae. 18 samples. Type: Expression profiling by high throughput sequencing.
SciPhy: A Bayesian phylogenetic framework using sequential genetic lineage tracing data
GEO Series GSE315827. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.
DXC Framework Windows
This is the DXC Framework for Windows Operating Systems
DXC Framework Linux
DXC Framework for Linux and Unix type operating systems
Dataset for the Paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study"
<p>This is the dataset for the paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study". It contains the 3004 Self-Claimed Assumptions (SCAs) extracted from nine deep learning frameworks (i.e., TensorFlow, Theano, PyTorch, Caffe, MXNet, Keras, CNTK, DL4J, and PaddlePaddle), which includes 2232 unique SCAs that have different content and 772 duplicated SCAs that have the same content as one of the 2232 SCAs.</p>
Dataset for the Paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study"
<p>This is the dataset for the paper: "Understanding Self-Claimed Assumptions in Deep Learning Frameworks: An Exploratory Study". It contains the 3084 Self-Claimed Assumptions (SCAs) extracted from nine deep learning frameworks (i.e., TensorFlow, Theano, PyTorch, Caffe, MXNet, Keras, CNTK, DL4J, and PaddlePaddle), which includes 2278 unique SCAs that have different content and 806 duplicated SCAs that have the same content as one of the 2278 SCAs.</p>
Trustworthiness of Artificial Intelligence – Powered Autonomous Healthcare Systems: An Assessment Framework Informed by Experts Insights
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Data for "Twin- model framework development for a comprehensive battery lifetime prediction validated with a realistic driving profile"
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GSS Customer-Centric Enterprise Architecture Framework QDC Codes from Thematic Analysis
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ScienceDex guides
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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.