cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26680466.v1
title: Additional file 4 of Transcriptomic analysis of intestinal organoids, derived from pigs divergent in feed efficiency, and their response to Escherichia coli

description: Additional file 4. Table with expression and statics from DESeq2 and edgeR analyses of significantly differential expressed genes after e-coli challenge in colon (high and low FE) and ileum (low FE).
DOI: 10.15468/dl.93bg66
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((148.472809 -32.656695, 148.475054 -32.656695, 148.475054 -32.652204, 148.472809 -32.652204, 148.472809 -32.656695)), ((148.472809 -32.656695, 148.466071 -32.656695, 148.466071 -32.658941, 148.468317 -32.658941, 148.468317 -32.661187, 148.475054 -32.661187, 148.475054 -32.658941, 148.472809 -32.658941, 148.472809 -32.656695)), ((148.475054 -32.658941, 148.4773 -32.658941, 148.4773 -32.661187, 148.481792 -32.661187, 148.481792 -32.658941, 148.484038 -32.658941, 148.484038 -32.656695, 148.475054 -32.656695, 148.475054 -32.658941)), ((148.475054 -32.661187, 148.475054 -32.665678, 148.4773 -32.665678, 148.4773 -32.661187, 148.475054 -32.661187)), ((148.484038 -32.658941, 148.484038 -32.661187, 148.495267 -32.661187, 148.495267 -32.658941, 148.497512 -32.658941, 148.497512 -32.661187, 148.499758 -32.661187, 148.499758 -32.656695, 148.497512 -32.656695, 148.497512 -32.652204, 148.488529 -32.652204, 148.488529 -32.658941, 148.484038 -32.658941)), ((148.499758 -32.661187, 148.499758 -32.665678, 148.490775 -32.665678, 148.490775 -32.667924, 148.50425 -32.667924, 148.50425 -32.672416, 148.506496 -32.672416, 148.506496 -32.665678, 148.50425 -32.665678, 148.50425 -32.661187, 148.499758 -32.661187)), ((148.506496 -32.672416, 148.506496 -32.676907, 148.515479 -32.676907, 148.515479 -32.674662, 148.510987 -32.674662, 148.510987 -32.672416, 148.506496 -32.672416)))"
 ]
}

 The dataset includes 275 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067336-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.23849541.v1
title: Additional file 2 of Lactiplantibacillus plantarum, lactiplantibacillus pentosus and inulin meal inclusion boost the metagenomic function of broiler chickens

description: Supplementary Material 2. Additional file 2 — Table S4. List of KEGG Orthology (KO) genes differentially (FRD &lt; 0.05) detected in the metagenome between, broilers fed with control (C) and Inulin plus probiotics (MIX). Positive log 2 fold changes indicate genes most abundant in C samples. Table S5. List of KEGG Orthology (KO) genes differentially (FRD &lt; 0.05) detected in the metagenome, between broilers fed with control (C) or Inulin (I). Positive log 2 fold changes indicate genes most abundant in C samples. Table S6. List of KEGG Orthology (KO) genes differentially (FRD &lt; 0.05) detected in the metagenome, between broilers fed with control (C) or and probiotics (LABs). Positive log 2 fold changes indicate genes most abundant in LABs samples.
DOI: 10.6084/m9.figshare.26571319.v1
title: Additional file 2 of Are deep models in radiomics performing better than generic models? A systematic review

description: Additional file 2.
DOI: 10.15468/dl.mgkfhu
title: Occurrence Download

description: A dataset containing 8737 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Myrmica sabuleti Meinert, 1861"
 ]
}

 The dataset includes 8737 records from 186 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067335-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26680517
title: Additional file 2 of Co-transfer of IncFII/IncFIB and IncFII plasmids mediated by IS26 facilitates the transmission of mcr-8.1 and tmexCD1-toprJ1

description: Additional file 2: Table S2. Plasmids Carrying mcr-8 and tmexCD-toprJ with Related Information.
DOI: 10.6084/m9.figshare.26680517.v1
title: Additional file 2 of Co-transfer of IncFII/IncFIB and IncFII plasmids mediated by IS26 facilitates the transmission of mcr-8.1 and tmexCD1-toprJ1

description: Additional file 2: Table S2. Plasmids Carrying mcr-8 and tmexCD-toprJ with Related Information.
DOI: 10.6084/m9.figshare.26608407.v1
title: Additional file 2 of Prognostic implication of downregulated exosomal miRNAs in patients with sepsis: a cross-sectional study with bioinformatics analysis

description: Additional file 2: Quantity and quality of the exosomal RNA. Details are described in the Excel file.
DOI: 10.6084/m9.figshare.26680547
title: Additional file 2 of Redevelopment of mental health first aid guidelines for substance use problems: a Delphi study

description: Additional file 2: Endorsed and rejected statements
DOI: 10.6084/m9.figshare.26680547.v1
title: Additional file 2 of Redevelopment of mental health first aid guidelines for substance use problems: a Delphi study

description: Additional file 2: Endorsed and rejected statements
DOI: 10.6084/m9.figshare.26680553
title: Additional file 1 of Germline mutations of homologous recombination genes and clinical outcomes in pancreatic cancer: a multicenter study in Taiwan

description: Additional file 1: Figure S1. Median overall survival of 527 PDAC patients with or without germline gene mutations in different stage. Figure S2. Median overall survival of 107 stage III PDAC patients with or without conversion surgery. Figure S3. Timeline comparison between the investigator-initiated trials for pancreatic cancer in Taiwan and the corresponding global phase III trials. Figure S4. The distribution of the most commonly used regimen by year. An arrow indicates the time of nab-paclitaxel reimbursement in Taiwan. Figure S5. Median overall survival of 320 PDAC patients with or without enrollment in clinical trials. Figure S6. (A) Median overall survival of 363 stage III/IV PDAC patients treated by different regimens. (B) Median overall survival of 363 stage III/IV PDAC patients with or without HR gene mutations treated with 1L platinum-based or non-platinum-based chemotherapy. Table S1. Categories of the 80 candidate cancer-associated genes analyzed for germline mutations in PDAC patients. Genes that have overlapping properties are listed only once and were classified as low-, moderate-, high- and recessive penetrance. Table S2. Pathogenic and likely pathogenic germline variants in susceptibility genes. Table S3. Family history of cancer in 527 PDAC patients. Table S4. Demographics of 32 patients with germline mutation in 12 homologous recombination genes treated with first-line platinum chemotherapy or non-platinum chemotherapy. Table S5. List of all regimen used in 368 stage III/IV PDAC patients.
DOI: 10.6084/m9.figshare.26680553.v1
title: Additional file 1 of Germline mutations of homologous recombination genes and clinical outcomes in pancreatic cancer: a multicenter study in Taiwan

description: Additional file 1: Figure S1. Median overall survival of 527 PDAC patients with or without germline gene mutations in different stage. Figure S2. Median overall survival of 107 stage III PDAC patients with or without conversion surgery. Figure S3. Timeline comparison between the investigator-initiated trials for pancreatic cancer in Taiwan and the corresponding global phase III trials. Figure S4. The distribution of the most commonly used regimen by year. An arrow indicates the time of nab-paclitaxel reimbursement in Taiwan. Figure S5. Median overall survival of 320 PDAC patients with or without enrollment in clinical trials. Figure S6. (A) Median overall survival of 363 stage III/IV PDAC patients treated by different regimens. (B) Median overall survival of 363 stage III/IV PDAC patients with or without HR gene mutations treated with 1L platinum-based or non-platinum-based chemotherapy. Table S1. Categories of the 80 candidate cancer-associated genes analyzed for germline mutations in PDAC patients. Genes that have overlapping properties are listed only once and were classified as low-, moderate-, high- and recessive penetrance. Table S2. Pathogenic and likely pathogenic germline variants in susceptibility genes. Table S3. Family history of cancer in 527 PDAC patients. Table S4. Demographics of 32 patients with germline mutation in 12 homologous recombination genes treated with first-line platinum chemotherapy or non-platinum chemotherapy. Table S5. List of all regimen used in 368 stage III/IV PDAC patients.
DOI: 10.6084/m9.figshare.26571274.v1
title: Additional file 11 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 11. Table S6. Cell deconvolution analysis of current bulk RNA-seq using published scRNA-seq dataset from the mouse fetal brain at E15.5.
DOI: 10.6084/m9.figshare.26571259.v1
title: Additional file 6 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 6. Table S1. Protocol for mouse model of prenatal stress.
DOI: 10.6084/m9.figshare.26571259
title: Additional file 6 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 6. Table S1. Protocol for mouse model of prenatal stress.
DOI: 10.6084/m9.figshare.26571262
title: Additional file 7 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 7. Table S2. RNA-seq QC, N=9 control, 7 stress.
DOI: 10.6084/m9.figshare.26571268
title: Additional file 9 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 9. Table S4A. Top 10 pathways in positive and negative GSEA results of all differential expressed genes, Stress v.s. Control. N=9 control, 7 stress.
DOI: 10.6084/m9.figshare.26571265.v1
title: Additional file 8 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 8. Table S3A. Differential genes list of RNA-seq, Stress v.s. Control. Padj &lt; 0.05; N=9 control, 7 stress. Table S3B. GO analysis of DEGs, Stress v.s. Control. Padj &lt; 0.05; N=9 control, 7 stress. Table S3C. Differential genes list of male samples, Stress v.s. Control. Padj &lt; 0.05; N=5 control, 3 stress. Table S3D. GO analysis of DEGs from male samples, Stress v.s. Control. Padj &lt; 0.05; N=5 control, 3 stress. Table S3E. Differential genes list of female samples, Stress v.s. Control. Pvalue &lt; 0.05; N=4 control, 4 stress. Table S3F. GO analysis of DEGs from female samples, Stress v.s. Control. Pvalue &lt; 0.05; N=4 control, 4 stress.
DOI: 10.6084/m9.figshare.26680580
title: Additional file 1 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26680580.v1
title: Additional file 1 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26680583
title: Additional file 2 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.26680583.v1
title: Additional file 2 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 2
DOI: 10.6084/m9.figshare.26680586
title: Additional file 3 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.26680586.v1
title: Additional file 3 of Integrated transcriptome and metabolome analysis provides insights into blue light response of Flammulina filiformis

description: Supplementary Material 3
DOI: 10.6084/m9.figshare.26571271.v1
title: Additional file 10 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 10. Table S5A. Cell type specific enrichment analysis of Class with up-regulated genes. Log2FC &gt; 0; Padj &lt; 0.05. N=9 control, 7 stress. Table S5B. Cell type specific enrichment analysis of Class with down-regulated genes. Log2FC &lt; 0; Padj &lt; 0.05. N=9 control, 7 stress. Table S5C. Cell type specific enrichment analysis of SubClass with up-regulated genes. Log2FC &gt; 0; Padj &lt; 0.05. N=9 control, 7 stress. Table S5D. Cell type specific enrichment analysis of SubClass with down-regulated genes. Log2FC &lt; 0; Padj &lt; 0.05. N=9 control, 7 stress.
DOI: 10.6084/m9.figshare.26571265
title: Additional file 8 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 8. Table S3A. Differential genes list of RNA-seq, Stress v.s. Control. Padj &lt; 0.05; N=9 control, 7 stress. Table S3B. GO analysis of DEGs, Stress v.s. Control. Padj &lt; 0.05; N=9 control, 7 stress. Table S3C. Differential genes list of male samples, Stress v.s. Control. Padj &lt; 0.05; N=5 control, 3 stress. Table S3D. GO analysis of DEGs from male samples, Stress v.s. Control. Padj &lt; 0.05; N=5 control, 3 stress. Table S3E. Differential genes list of female samples, Stress v.s. Control. Pvalue &lt; 0.05; N=4 control, 4 stress. Table S3F. GO analysis of DEGs from female samples, Stress v.s. Control. Pvalue &lt; 0.05; N=4 control, 4 stress.
DOI: 10.6084/m9.figshare.26571268.v1
title: Additional file 9 of Transcriptomic profiling of the developing brain revealed cell-type and brain-region specificity in a mouse model of prenatal stress

description: Additional file 9. Table S4A. Top 10 pathways in positive and negative GSEA results of all differential expressed genes, Stress v.s. Control. N=9 control, 7 stress.
DOI: 10.6084/m9.figshare.26608368
title: Additional file 4 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

description: Additional file 4: Table S5. Cluster of genes identified by Kmeans clustering based on the pattern of genes’ differential expression across the cell types for DPD deficiency. Each column represents the genes belonging to the same cluster.
DOI: 10.6084/m9.figshare.26608362.v1
title: Additional file 2 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

description: Additional file 2: Table S2. Differential gene expression analysis for DPD deficiency. The summary of differential gene expression analysis using Limma package for bulk, pseudo-bulk, and deconvolved cell type or pairs of cell types (CN-NEU, CBC-INTER) expression profiles (“DGE”). Top 200 DE genes per cell type or pairs of cell types are listed in “Top 200 DE genes per cell type” sheet.
DOI: 10.6084/m9.figshare.26608407
title: Additional file 2 of Prognostic implication of downregulated exosomal miRNAs in patients with sepsis: a cross-sectional study with bioinformatics analysis

description: Additional file 2: Quantity and quality of the exosomal RNA. Details are described in the Excel file.
DOI: 10.6084/m9.figshare.26608371
title: Additional file 5 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

description: Additional file 5: Table S6. Summary of David gene set characterization for cluster of genes identified by Kmeans algorithm based on the pattern of genes’ differential expression across the cell types for DPD deficiency. The David results for each cluster are included in a sheet named with the cluster name (Cluster X). Clusters zero and four were excluded as they had more than 2000 genes. David results for top 200 DE genes identified by DGE analysis on bulk and bootstrapped pseudo-bulk are included for convenient comparison.
DOI: 10.6084/m9.figshare.26608377
title: Additional file 7 of BEDwARS: a robust Bayesian approach to bulk gene expression deconvolution with noisy reference signatures

description: Additional file 7: Table S9. KnowEng gene set characterization performed on the markers of a subset of cell clusters for DPD deficiency. These results were used to assign cell types to cluster of non-affected and affected cells. The GO terms that were enriched and used for cell type assignment are summarized in Additional file 1: Table S8 for easier lookup.
DOI: 10.6084/m9.figshare.26680592
title: Additional file 1 of The potential of international large-scale assessments for meta-analyses in education

description: Additional file 1. Gender Differences in Academic Achievement.
DOI: 10.15156/bio/sh0188574.10fu
title: SH0188574.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0188575.10fu
title: SH0188575.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0188576.10fu
title: SH0188576.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0188577.10fu
title: SH0188577.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0188578.10fu
title: SH0188578.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0188579.10fu
title: SH0188579.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.