cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7915635
title: FUN-LDA scores for human genome assembly GRCh37

description: FUN-LDA is based on a Latent Dirichlet Allocation (LDA) model for predicting functional effects of non-coding genetic variants in a cell type and tissue-specific way by integrating diverse epigenetic annotations for specific cell types and tissues from large-scale genomics projects such as ENCODE and Roadmap Epigenomics. Using this unsupervised approach, we predict tissue-specific functional effects for every position in the human genome for 127 tissues and cell types in ENCODE and Roadmap Epigenomics. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Format</strong> The FUN-LDA scores are stored in the UCSC Genome Browser bigWig Track Format. To extract FUN-LDA scores, the bigWigAverageOverBed utility is required. It can be downloaded from the Genome Browser website at https://hgdownload.cse.ucsc.edu/admin/exe. User should prepare a UCSC Genome Browser bed file, a tab-separated four-column file. The first column is the chromosome; the second is the zero-based coordinate of the position of interest; the third is that zero-based coordinate plus one; and the fourth is a unique identifier for the position. The command below is an example of extracting FUN-LDA scores in tissue E007 for the positions defined in a bed file, "input.bed". <pre><code class="language-bash">bigWigAverageOverBed E007.valley9.c89.bigwig input.bed output.tab</code></pre> It produces a six-column file, output.tab. The first column includes the unique position id defined in the input bed file. The last column, average over the covered bases, is the score for this position. <strong>Reference</strong> Daniel Backenroth, Zihuai He, Krzysztof Kiryluk, Valentina Boeva, Lynn Pethukova, Ekta Khurana, Angela Christiano, Joseph Buxbaum, Iuliana Ionita-Laza. FUN-LDA: A latent Dirichlet allocation model for predicting tissue-specific functional effects of noncoding variation: Methods and applications. American Journal of Human Genetics, 2018.<br>
DOI: 10.5281/zenodo.3336209
title: GenoNet scores for human genome assembly GRCh37

description: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants. <strong>Format</strong> The GenoNet scores are stored in the tab-delimited text files. Each row represents a genomic region with 131 columns. Please find the header line in "genonet.header.txt". The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025. chr1 10000 10025 chr1_10001_10025 Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018. <strong>Release</strong> GRCh37 https://zenodo.org/record/3336209 GRCh38 liftover https://zenodo.org/record/6484230
DOI: 10.5281/zenodo.3336208
title: GenoNet scores for human genome assembly GRCh37

description: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants. <strong>Format</strong> The GenoNet scores are stored in the tab-delimited text files. Each row represents a genomic region with 131 columns. Please find the header line in "genonet.header.txt". The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025. chr1 10000 10025 chr1_10001_10025 Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018. <strong>Release</strong> GRCh37 https://zenodo.org/record/3336209 GRCh38 liftover https://zenodo.org/record/6484230
DOI: 10.7281/t1/dp2u9m/0voh80
title: File Description.docx

description: Description of file content.
DOI: 10.7281/t1/dp2u9m/j7eops
title: W1_N152.tab

description: Responses to a web survey of the Doubt Questionnaire (N=152)
DOI: 10.7281/t1/dp2u9m/jo4rya
title: RDMPilotRDM_GIANT_VC_Repository.tab

description: Results of RDM task
DOI: 10.7281/t1/dp2u9m/jxfrdd
title: W3_TESTRETEST_N158.xlsx

description: Responses to a test-retest survey of the Doubt Questionnaire (N=158)
DOI: 10.7281/t1/dp2u9m/m9scsp
title: C1_N94R.tab

description: DQ scores in OCD cases and comparison group (N=94)
DOI: 10.7281/t1/dp2u9m/4moslc
title: CSV_versions.zip

description: Copies of data files and codebooks in CSV format
DOI: 10.7281/t1/dp2u9m/5nn13f
title: CSV_versions.zip

description: Copies of data files and codebooks in CSV format
DOI: 10.7281/t1/dp2u9m/jlnw26
title: File Description.pdf

description: Description of file content (PDF version)
DOI: 10.5281/zenodo.3635844
title: GenoNet scores for human genome assembly GRCh38

description: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type-specific epigenetic annotations to predict functional consequences of non-coding variants. <strong>Format</strong> The GenoNet scores are stored in the tab-delimited text files. Each row represents a genomic region with 131 columns. Please find the header line in "genonet.header.txt". The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025. chr1 10000 10025 chr1_10001_10025 Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018. <strong>Release</strong> GRCh37 https://zenodo.org/record/3336209 GRCh38 liftover https://zenodo.org/record/6484230
description: {"references": ["Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018."]}
DOI: 10.5281/zenodo.3635845
title: GenoNet scores for human genome assembly GRCh38

description: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type-specific epigenetic annotations to predict functional consequences of non-coding variants.
description: {"references": ["Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018."]}
DOI: 10.5281/zenodo.6484230
title: GenoNet scores for human genome assembly GRCh38

description: Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type-specific epigenetic annotations to predict functional consequences of non-coding variants. <strong>Format</strong> The GenoNet scores are stored in the tab-delimited text files. Each row represents a genomic region with 131 columns. Please find the header line in "genonet.header.txt". The first four columns are chromosome, start coordinate, end coordinate, and a region ID named by positions. Please note that the coordinates are counted in the 0-based UCSC Genome Browser BED format. For example, the following region with a start position 10000 and an end position 10025 includes 25 base pairs within chr1:10001-10025. chr1 10000 10025 chr1_10001_10025 Columns 5-131 are the predicted tissue-specific functional effects (GenoNet scores) for the 127 Roadmap tissues. Each column is named by the corresponding epigenome ID. This online spreadsheet includes the information about the 127 Roadmap tissues in detail. <strong>Reference</strong><br> Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018. <strong>Release</strong> GRCh37 https://zenodo.org/record/3336209 GRCh38 liftover https://zenodo.org/record/6484230
description: {"references": ["Zihuai He, Linxi Liu, Kai Wang, Iuliana Ionita-Laza. A semi-supervised approach for predicting cell type/tissue specific functional consequences of non-coding variation using massively parallel reporter assays. Nature Communications, 2018."]}
DOI: 10.6084/m9.figshare.22789901
title: data_fxcons.dta

description: <strong>KLIPS (Korean Labor & Income Panel Study) is a longitudinal survey of the labor market / income activities of households and individuals residing in urban areas. The 1st Wave of the KLIPS was launched by the KLI (Korea Labor Institute) in 1998</strong>
DOI: 10.5067/gpm/amsre/aqua/1c/07
title: GPM AMSR-E on AQUA Common Calibrated Brightness Temperatures L1C 1.5 hours 10.5 km V07
DOI: 10.6084/m9.figshare.22790000
title: Marmosops incanus' occurrence records and morphological data

description: This text-based matrix contains the entire occurrence records of <em>Marmosops incanus</em> used for model calibration and its morphological data used in intraspecific variability analyses.
DOI: 10.25345/c5js9hj5m
title: MassIVE MSV000091895 - seale_microscale_timsTOF_ProHits_2
title: This is the second test of our ProHits submission system for MassIVE using timsTOF data and MSFragger/FragPipe search results
DOI: 10.34740/kaggle/dsv/5646237
title: Balochi Language to English Translation Dataset
DOI: 10.5281/zenodo.4701390
title: Eigen software for functional genomic annotations

description: Eigen is a spectral approach integrating functional genomic annotations for coding and noncoding variants. The training and testing datasets used in the Eigen paper and the R scripts implementing model training and calculation of Eigen scores are available for download. Information about the paper and the precomputed genome-wide Eigen scores is available at http://www.funlda.com/toolkit.
DOI: 10.5281/zenodo.4701391
title: Eigen software for functional genomic annotations

description: Eigen is a spectral approach integrating functional genomic annotations for coding and noncoding variants. The training and testing datasets used in the Eigen paper and the R scripts implementing model training and calculation of Eigen scores are available for download. Information about the paper and the precomputed genome-wide Eigen scores is available at http://www.funlda.com/toolkit.
DOI: 10.5281/zenodo.4046257
title: Eigen scores for human genome assembly GRCh37 Part 1 (Chr1 - Chr3)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.4046258
title: Eigen scores for human genome assembly GRCh37 Part 1 (Chr1 - Chr3)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.6084/m9.figshare.22753637
title: Pollinator visits tomato flowers in blocks with imidacloprid sprayed once and twice weekly and control block.

description: Pollinator visits to tomato flowers in blocks with imidacloprid sprayed once and twice a week and control block.
DOI: 10.6084/m9.figshare.22753637.v1
title: Pollinator visits tomato flowers in blocks with imidacloprid sprayed once and twice weekly and control block.

description: Pollinator visits to tomato flowers in blocks with imidacloprid sprayed once and twice a week and control block.
DOI: 10.5281/zenodo.4046966
title: Eigen scores for human genome assembly GRCh37 Part 2 (Chr4 - Chr8)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.4046967
title: Eigen scores for human genome assembly GRCh37 Part 2 (Chr4 - Chr8)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.6084/m9.figshare.22790039
title: Supplementary Material

description: Supplementary Material
DOI: 10.6084/m9.figshare.22790039.v1
title: Supplementary Material

description: Supplementary Material
DOI: 10.5281/zenodo.4047193
title: Eigen scores for human genome assembly GRCh37 Part 3 (Chr9 - Chr16)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.4047192
title: Eigen scores for human genome assembly GRCh37 Part 3 (Chr9 - Chr16)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.7916381
title: Local-scale patterns of coastal hypoxia in an upwelling region

description: GENERAL INFORMATION Title of Dataset: Replication data for: - Sea Surface Temperature (SST, daily) MODIS-Aqua (2016-2019)
DOI: 10.5281/zenodo.7916380
title: Local-scale patterns of coastal hypoxia in an upwelling region

description: GENERAL INFORMATION Title of Dataset: Replication data for: - Sea Surface Temperature (SST, daily) MODIS-Aqua (2016-2019)
DOI: 10.5281/zenodo.4048100
title: Eigen scores for human genome assembly GRCh37 Part 4 (Chr17 - Chr22)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.4048099
title: Eigen scores for human genome assembly GRCh37 Part 4 (Chr17 - Chr22)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
DOI: 10.5281/zenodo.3256670
title: Eigen scores for human genome assembly GRCh38 Part 1 (Chr12 - Chr22)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.
description: Uploading errors
DOI: 10.5281/zenodo.3256671
title: Eigen scores for human genome assembly GRCh38 Part 1 (Chr12 - Chr22)

description: Eigen is a spectral approach to the functional annotation of genetic variants in coding and noncoding regions. Eigen makes use of a variety of functional annotations in both coding and noncoding regions (such as protein function scores, evolutionary conservation scores, and epigenetic annotations from ENCODE and Roadmap Epigenomics projects), and combines them into one single measure of functional importance. Eigen is an unsupervised approach, and, unlike many existing methods, is not based on any labelled training data. Eigen produces estimates of predictive accuracy for each functional annotation score, and subsequently uses these estimates of accuracy to derive the aggregate functional score for variants of interest as a weighted linear combination of individual annotations.