cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3153193.v9
title: [CODE: Pre-processing]

description: This repository contains the scripts used for doing part of the Quality Control (QC) results presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the QC.
DOI: 10.6084/m9.figshare.3146794.v2
title: mschool2004

description: 银川市第十二中学七四级 2004 年师生联谊会随笔
DOI: 10.6084/m9.figshare.3146878.v3
title: Wikipedia Navigation Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3146740
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DMRs]

description: This repository contains the scripts used to identify differentially methylated regions (DMRs) in the reference below.<br><br>The complete details about the medipDE.R code and the commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146878.v4
title: Wikipedia Navigation Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3146710
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 2: qPCR validations]

description: <b>DESCRIPTION</b><br>qPCR was used to validate the integrity of sequencing libraries of different ChIP-seq samples, as well as validate particular differentially methylated genes<br><br><b>FILES</b><br>Validation of ChIP-seq library enrichment:<br><b>20130426-CA1-1h-K27ac-K79me3-K4me3-H3-lib_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K27ac, H3K79me3, H3K4me3 or H3. The target genes were NeuN, Chd4, GAPDH, InterG and cFOS.<br><b>20130715-ACCnaiveK76me3K27ac_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from ACC neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K79me3 or H3K27ac. The target genes were NeuN, Chd4 and GAPDH.<br><b>20131125-CA1-Naive_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for naïve (N) samples from CA1 neuronal or non-neuronal cells (+ or -, respectively) and immunoprecipitated for H3K27ac, H3K27me3, H3K79me3, H3K4me3, H3 or H3K4me1. The target genes were TFF1, GAPDH, InterG and NeuN.<br><b>20150122-CA1-K9ac_qPCR.xlsx</b> – this file contains tables with qPCR values from validations of ChIP-seq libraries for samples from ACC neuronal or non-neuronal cells (+ or -, respectively) taken 1 hour (1h) after naïve (N), context (Ct) or context-shock (FC) and immunoprecipitated for H3K9ac. The target genes were cFosF2R2, cFosF3R3, GAPDH, InterG and NeuN.<br><br>Validation of differentially methylated genes:<br><b>20150729-MeDIP-LIB-ACC_qPCR.xlsx</b> – this file contains tables with qPCR values for samples taken from ACC tissue 1 hour (1h) after naïve (N), context (Ct) or context-shock (CS). The target genes were <i>Loxhd1</i>, <i>Vrk1</i>, <i>GAPDH</i>(-) and <i>Cobl</i>.<br><b>20150802-ACC-lib_qPCR.xlsx</b> – this file contains tables with qPCR values for samples taken from ACC tissue 1 hour (1h) or 4 weeks (4w) after naïve (N), context (C) or context-shock (CS). The target genes were <i>Fgfr2</i>, <i>Nova1</i> and <i>Reelin</i>.<br><br><b>DATA GENERATION</b><br>qPCR experiments were run on Roche LightCycler 480, and the machine was used to export the Ct values. The resulting output was imported into pyQPCR (version 0.10dev) using dilutions of inputs as standards, along with the following parameters:<br>Calculation type: Absolute quantification<br>Confidence interval: 90.00%<br>Machine: Roche LightCycler 480<br>Maximum E(Ct): 0.30<br>Minimum Ct: 35.00<br><br>
DOI: 10.6084/m9.figshare.3153193.v10
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: Pre-processing]

description: This repository contains the scripts used for doing part of the Quality Control (QC) results presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the QC.
DOI: 10.6084/m9.figshare.3146749.v2
title: DEE and DEG code

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the analyses.
DOI: 10.6084/m9.figshare.3146878.v5
title: Wikipedia Navigation Vectors

description: In this project, we learned embeddings for Wikipedia articles and Wikidata items by applying Word2vec models to a corpus of reading sessions.Although Word2vec models were developed to learn word embeddings from a corpus of sentences, they can be applied to any kind of sequential data. The learned embeddings have the property that items with similar neighbors in the training corpus have similar representations (as measured by the cosine similarity, for example). Consequently, applying Wor2vec to reading sessions results in article embeddings, where articles that tend to be read in close succession have similar representations. Since people usually generate sequences of semantically related articles while reading, these embeddings also capture semantic similarity between articles.There have been several approaches to learning vector representations of Wikipedia articles that capture semantic similarity by using the article text or the links between articles. An advantage of training Word2vec models on reading sessions, is that they learn from the actions of millions of humans who are using a diverse array of signals, including the article text, links, third-party search engines, and their existing domain knowledge, to determine what to read next in order to learn about a topic.An additional feature of not relying on text or links, is that we can learn representations for Wikidata items by simply mapping article titles within each session to Wikidata items using Wikidata sitelinks. As a result, these Wikidata vectors are jointly trained over reading sessions for all Wikipedia language editions, allowing the model to learn from people across the globe. This approach also overcomes data sparsity issues for smaller Wikipedias, since the representations for articles in smaller Wikipedias are shared across many other potentially larger ones. Finally, instead of needing to generate a separate embedding for each Wikipedia in each language, we have a single model that gives a vector representation for any article in any language, provided the article has been mapped to a Wikidata item.For detailed documentation, see the wiki page.
DOI: 10.6084/m9.figshare.3153193.v11
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: Pre-processing]

description: This repository contains the scripts and files used to generate the alignment files used for downstream analyses, the bigWig files used to visualise the data and various Quality Control (QC) results used to address the quality of the various samples.<br><br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146749.v3
title: CODE: DEE and DEG

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the analyses.
DOI: 10.6084/m9.figshare.3153193.v13
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: Pre-processing]

description: This repository contains the scripts and files used to generate the alignment files used for downstream analyses, the bigWig files used to visualise the data and various Quality Control (QC) results used to address the quality of the various samples.<br><br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3153193.v12
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: Pre-processing]

description: This repository contains the scripts and files used to generate the alignment files used for downstream analyses, the bigWig files used to visualise the data and various Quality Control (QC) results used to address the quality of the various samples.<br><br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146749.v4
title: CODE: DEE and DEG

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to run the code for doing the analyses.
DOI: 10.6084/m9.figshare.3146740.v3
title: DMR code

description: The zip file contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Instructions----------------1. Unzip the file in a linux computer$unzip DMRscripts<br>2. Follow the description contained in the README.txt file to run a DMR analysis<br>
DOI: 10.6084/m9.figshare.3146749.v6
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DEEs and DEGs]

description: This repository contains the scripts used to identify differentially expressed genes (DEGs) and differentially expressed exons (DEEs) in the reference below.<br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146749.v5
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DEE and DEG]

description: This repository contains the scripts used to identify differentially expressed genes (DEGs) and differentially expressed exons (DEEs) in the reference below.<br>The complete details about the various scripts and files in this repository, as well as the set of commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146740.v4
title: DMR code

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to do the analysis.
DOI: 10.6084/m9.figshare.3146698
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 8: DEEs and DEGs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially expressed genes (DEG) and exons (DEE), where each table contains comparisons between the conditions naive and context (N-C) and naive and context-shock (N-CS) for each time point (1h or 4w) and tissue (CA1 or ACC).<br><br><b>TABLES</b><br>DEG tables<br>Columns "ensemblID", "gene_name" and "description" refer to the ENSEMBL ID, gene symbol and gene description, respectively. Columns "chromosome", "start", "end" and "strand" indicate the position of the gene (with strand being "1" for positive strand or "-1" for negative strand). Column "biotype" refers to the type of gene (protein-coding, antisense, non-coding etc.). Finally, column "baseMean" refers to the mean expression of the gene in all samples, column "log2FoldChange" refers to the log2 fold change between the samples in the different conditions, and columns "pvlaue" and "padj" refer to the raw and Benjamini-Hochberg-adjusted p-values, respectively.<br>DEE tables<br>Column "ensemblID" still refers to the ENSEMBL gene ID, but some genes may appear multiple times, as they contain multiple exons, and the column "exonID" indicates the exon number in the gene. Columns “pvalue”, “padj”, “meanBase” and “log2FoldChange” correspond to the same columns in the DEG tables (with the exception of “meanBase”, which is equivalent to “baseMean” in the DEG tables).<br><br><b>DATA GENERATION</b><br>The code used to do differential expression analyses of genes and exons can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146749).<br><br>
DOI: 10.6084/m9.figshare.3146740.v5
title: DMR code

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to do the analysis.
DOI: 10.6084/m9.figshare.3146740.v6
title: DMR code

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to do the analysis.
DOI: 10.6084/m9.figshare.3146698.v1
title: Differentially expressed exons and genes

description: List of differentially expressed exons and genes
DOI: 10.6084/m9.figshare.3146740.v7
title: CODE: DMR

description: This repository contains the scripts used for doing the differential methylation analyses presented in the reference below.<br>Please consult the README.txt file for instructions on how to do the analysis.
DOI: 10.6084/m9.figshare.3146698.v2
title: 6.DEEs and DEGs

description: List of differentially expressed exons and genes
DOI: 10.6084/m9.figshare.3146740.v9
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DMRs]

description: This repository contains the scripts used to identify differentially methylated regions (DMRs) in the reference below.<br><br>The complete details about the medipDE.R code and the commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146740.v8
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [CODE: DMR]

description: This repository contains the scripts used to identify differentially methylated regions (DMRs) in the reference below.<br><br>The complete details about the medipDE.R code and the commands used to run the analysis, are included in the file README.txt
DOI: 10.6084/m9.figshare.3146698.v3
title: (6) DEEs and DEGs

description: List of differentially expressed exons and genes
DOI: 10.6084/m9.figshare.3146695
title: Genome-wide chromatin and gene expression profiling during memory formation and maintenance in adult mice. [Data Record 7: DMRs]

description: <b>DESCRIPTION</b><br>This data record contains tables of differentially methylated regions (DMRs), where each table contains different compared conditions for each time point (1h or 4w), tissue (CA1 or ACC) and cell type (Neu or Non). Tables with the suffix "_raw" contain all DMRs for adjusted p-value threshold of 0.5, and all other tables contain annotated DMRs for adjusted p-value 0.1 in C-CS tests or for adjusted p-value 0.05 in N-C and N-CS tests. The compared conditions concern the different sets of mouse, N-C compares naïve (N) to context only (C) mice, N-CS compares naive (N) to context shock (CS) mice, and C-CS compares context only (C) to context shock (CS) mice.<br><b><br>DATA REPOSITORIES</b><br>EPIGENOME BROWSER (https://memory-epigenome-browser.dzne.de/)<br>Contains the visualisation tracks for the DMRs (i.e. positions of DMRs) under category "DMR-DATA". Sample names include compared conditions (e.g. CxCS compares context with context-shock), time-point, tissue and cell-type separated by dashes. GFF files can also be downloaded for local visualisation.<br>Figshare<br>For the annotated DMRs, the tables contain columns "Gene name", "Gene type", "Transcript ID" and "Gene structure", corresponding to the gene name, type (protein_coding, long non-coding, miRNA etc.), ENSEMBL transcript ID and structure (intronic, downstream, etc.) for the nearest gene to each DMR. For all tables, The columns “chromosome”, “DMR start” and “DMR end” contain the position in the genome where the DMR was detected (mm10 assembly). The column “logFC” shows the fold change of the comparison between the conditions in log 2. Columns “pvalue” and “p-adjust” contain the pvalue and the adjusted p-value according to the FDR test.<br><br><b>DATA GENERATION</b><br>The code used to identify DMRs can be found in Figshare (https://dx.doi.org/10.6084/m9.figshare.3146740).<br><br>
DOI: 10.6084/m9.figshare.3146698.v4
title: (6) DEEs and DEGs

description: List of differentially expressed exons and genes
DOI: 10.6084/m9.figshare.3146695.v1
title: DMRs

description: List of DHPTMs
DOI: 10.6084/m9.figshare.3146692.v1
title: DHPTMs

description: Differential Histone Post-translational modifications
DOI: 10.6084/m9.figshare.3146698.v5
title: [Data Record 6]: DEEs and DEGs

description: List of differentially expressed exons and genes
DOI: 10.6084/m9.figshare.3146710.v9
title: [Data Record 1]: qPCR validations

description: <b>DESCRIPTION</b><br>qPCR was used to assess the enrichment of ChIP and MeDIP experiments and to validate the integrity of sequencing libraries<br><br><b>FILES</b><br>Validation of ChIP-seq library enrichment:<br><b>20130426-CA1-1h-K27ac-K79me3-K4me3-H3-lib_qPCR.xlsx</b> - validation of ChIP-seq libraries for samples from CA1 taken 1 hour after conditioning and immunoprecipitated for H3K27ac, H3K79me3, H3K4me3 or H3<br><b>20130715-ACCnaiveK76me3K27ac_qPCR.xlsx</b> - validation of ChIP-seq libraries for naive samples from ACC and immunoprecipitated for H3K76me3 or H3K27ac<br><b>20131125-CA1-Naive_qPCR.xlsx</b> - validation of ChIP-seq libraries for naive samples from CA1<br><b>20150122-CA1-K9ac_qPCR.xlsx</b> - validation of ChIP-seq libraries for naive samples from CA1 and immunoprecipitated for H3k9ac<br><br>Validation of MedIP-seq library enrichment:<br><b>20150729-MeDIP-LIB-ACC_qPCR.xlsx</b> - MedIP-seq library validated for genes Cobl, Loxhd1, Vrk1 and GAPDHenh (-)<br><b>20150802-ACC-lib_qPCR.xlsx</b> - MedIP-seq library validated for genes Nova1 and Reelin<br><br><b>DATA GENERATION</b><br>qPCR experiments were run on Roche LighCycler 480, and the machine was used to export the Ct values. The resulting output was imported into pyQPCR (version 0.10dev) using dilutions of inputs as standards, along with the following parameters:<br><br>Calculation type: Absolute quantification<br>Confidence interval: 90.00%<br>Machine: Roche LighCycler 480<br>Maximum E(Ct): 0.30<br>Minimum Ct: 35.00<br>
DOI: 10.6084/m9.figshare.3146695.v2
title: 5.DMRs

description: List of DHPTMs
DOI: 10.6084/m9.figshare.3146689.v1
title: CRM predictions

description: RFECs CRM predictions
DOI: 10.6084/m9.figshare.3146692.v2
title: 4.DHPTMs

description: Differential Histone Post-translational modifications
DOI: 10.6084/m9.figshare.3146689.v2
title: 3.CRM predictions

description: RFECs CRM predictions