№ files_lp_4_process_2_60429
File format: docx
Character count: 28216
File size: 746 KB
Year:
2026
Field:
Genomics, Virology, Bioinformatics
Document type:
Supplementary material / research data
Authors:
Rui Dong, Lily He, Rong Lucy He, Stephen S.-T. Yau
Institution:
University of Illinois at Chicago
Target audience:
Researchers in virology and computational biology
Data sources:
GenBank
Organisms:
Coronaviruses, Influenza A viruses, Ebolaviruses
Sequence type:
Complete genomes
Number of sequences:
146
Tables included:
S1, S2, S3
Date published:
2026
Correspondence:
Prof. Stephen S.-T. Yau, [email protected]
Description:
Supplementary dataset presenting GenBank accession numbers, genome lengths, and classifications for 36 coronaviruses, 38 influenza A viruses, and 72 ebolaviruses, supporting a study on nucleotide covariance clustering.
Price: 8 / 10 USD
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The product description is provided for reference. Actual content and formatting may differ slightly.
Year:
2023
Region / city:
N/A
Topic:
Statistics / ANCOVA
Document type:
Educational / Tutorial
Organization / institution:
N/A
Author:
N/A
Target audience:
Students learning statistics
Period of action:
N/A
Approval date:
N/A
Date of changes:
N/A
Year:
1994–2013
Field:
Neuroscience
Topic:
Brain structural covariance networks in autism spectrum disorder
Document type:
Supplementary methodological documentation
Organization:
ENIGMA Consortium
Research group:
ENIGMA ASD Working Group
Source study title:
Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium
Diagnostic framework:
DSM-IV and DSM-IV-TR
Assessment instruments:
Autism Diagnostic Observation Schedule-Generic (ADOS)
Data type:
MRI structural imaging and cortical thickness measurements
Software used:
FreeSurfer (recon-all pipeline)
Quality control protocol:
ENIGMA imaging protocols
Participants with ASD:
Individuals across the autism spectrum with available MRI and clinical data
Control participants:
Typically developing individuals without ASD diagnosis
Network analysis methods:
Intra-individual structural covariance, clustering coefficient, shortest path length, small-worldness index
Datasets included:
43 datasets
Supplementary materials:
Supplementary methods (pages 2–6) and 20 supplementary tables in a separate spreadsheet file
Version:
1.0
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Training Date:
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Year:
2007
Note:
Region / City
Topic:
Failover Clustering, Server Management
Document Type:
Technical Overview
Organization / Institution:
Microsoft
Target Audience:
IT Professionals, System Administrators
Year:
2009
Region / city:
Worldwide
Topic:
SQL Server 2008 Failover Clustering
Document Type:
Technical Article
Organization / Institution:
Microsoft
Author:
Mike Weiner, Paul Burpo, Max Verun, Joseph Sack, Justin Erickson
Contributors:
Sanjay Mishra, Jason Wu, Uttam Parui
Target Audience:
Technical professionals
Period of validity:
N/A
Approval Date:
June 2009
Revision Date:
May 2013
Year:
2011
Region / City:
Not specified
Topic:
Hierarchical Clustering, PCMH Recognition
Document Type:
Technical Appendix
Organization:
Not specified
Author:
Not specified
Target Audience:
Researchers, policymakers, healthcare professionals
Period of validity:
Not specified
Date of approval:
Not specified
Date of modifications:
Not specified
Year:
2010
Region / city:
Worldwide
Topic:
Failover Clustering, Active Directory Certificate Services
Document Type:
Technical Guide
Organization / Institution:
Microsoft Corporation
Author:
Carsten B. Kinder, Mark B. Cooper
Target Audience:
IT professionals, system administrators
Period of validity:
Ongoing
Approval Date:
January 2010
Date of Changes:
Not specified
Year:
2017
Region / city:
United Kingdom
Topic:
Bitcoin; Price Clustering; Cryptocurrency
Document type:
Research Paper
Institution:
University of Southampton
Author:
Dr Andrew Urquhart
Target audience:
Academics, Investors, Researchers
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Source:
TCGA cancer cohort
Section:
Supplementary Materials
Content:
Supplementary figures and table
Methods:
Consensus clustering, pathway enrichment analysis, LASSO regression analysis
Molecular focus:
m6A-related lncRNAs
Number of interactions identified:
625
Number of m6A-related lncRNAs identified:
491
Data description:
Correlation coefficients, p-values, and regulation direction between m6A regulators and lncRNAs
Statistical parameters:
Correlation (cor), p-value, positive and negative regulation
Research field:
Cancer genomics
Type of material:
Supplementary figures and data table
Year:
2023
Region / city:
Not specified
Topic:
Chronic kidney disease, Genetic and phenotypic analysis
Document type:
Research paper
Institution / organization:
Not specified
Author:
Not specified
Target audience:
Researchers, healthcare professionals, geneticists
Period of validity:
Not specified
Approval date:
Not specified
Date of changes:
Not specified
Year:
2026
Region:
Coastal Japan
Topic:
Geophysical fluid dynamics, extreme events
Document type:
Research article
Institution:
Meteorological Research Institute, Japan Meteorological Agency; Graduate School of Science, Kyoto University
Authors:
Kunihiro Aoki, Hideyuki Nakano, Nariaki Hirose, Norihisa Usui, Kei Sakamoto, Takahiro Toyoda, Shogo Urakawa, Yuma Kawakami
Target audience:
Atmospheric and oceanic scientists, data scientists
Methodology:
Variational autoencoder with principal-component-scaled augmentation
Data scope:
Approximately 150 samples of coastal ocean velocity fields during Kyucho events
Results:
Identification of four distinct circulation modes
Application period:
Kyucho event study period
Abstract type:
Clustering and pattern analysis in small-sample geophysical datasets
Journal context:
Geophysical research, extreme event prediction
Course:
COSC 4335
Institution:
University-level computer science course
Instructor:
Dr. Eick
Document Type:
Course assignment
Subject Area:
Data Mining and Clustering Algorithms
Algorithms Covered:
K-means, DBSCAN
Programming Language:
R
Datasets:
Complex8 dataset; HAbalone dataset (modified Abalone dataset)
Original Dataset Source:
UCI Machine Learning Repository Abalone Dataset
Project Type:
Individual project
Learning Objectives:
Clustering analysis, interpretation of clustering results, R function development, unsupervised data mining analysis, use of background knowledge in data mining
Assignment Tasks:
Dataset transformation; implementation of purity evaluation function; clustering analysis with K-means and DBSCAN
Submission Deadline:
March 18, 2015, 11p (early submission bonus)
Final Deadline:
March 24, 2015, 11p
Document Version:
Fourth Draft
Last Updated:
Feb. 16, 2015, 2:30p
Year:
2026
Region / Institution:
Not specified
Topic:
Single-cell clustering methods
Document type:
Supplemental Material / Research Comparison
Author:
Not explicitly stated
Dataset:
Normalized single-cell dataset including CD19+ CD3- B cells
Methods compared:
PICAFlow, FlowSOM, PhenoGraph
Cell populations analyzed:
Lymphocyte-shaped single cells
Metrics evaluated:
Number of clusters, cluster abundance, marker positivity accuracy
Figures included:
S1A, S1B, S1C, S1D, S1E
Statistical analysis:
One-sample signed-rank Wilcoxon tests
Key observations:
PICAFlow identifies major clusters efficiently, merges rare populations, produces higher median abundance clusters, maintains closer accuracy to manual gating for most markers