cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.c.3633491_d2.v1
title: Additional file 3: of Spatial variability of the effect of air pollution on term birth weight: evaluating influential factors using Bayesian hierarchical models

description: Stage one nox: Code file for the stage one model for NOx. (R 5 kb)
DOI: 10.6084/m9.figshare.c.3631340_d7.v1
title: Additional file 5: Table S4. of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: Newly identified transcription regions. Description of data: Information about the 2650 unique transcripts assembled from new transcription regions. (XLS 1496Â kb)
DOI: 10.6084/m9.figshare.c.3631340_d5.v1
title: Additional file 3: Table S2. of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: List of differentially expressed splice variants in early growth fruits. Expression values (FPKM) of the 7959 differentially expressed splice variants in early growth fruits identified by comparison of gene expression between two consecutive stages. Splice variants with expression values of FPKM &gt; =1 and fold changes (FC) &gt; =4 were considered to be differentially expressed. (XLS 2480 kb)
DOI: 10.6084/m9.figshare.c.3631340_d6.v1
title: Additional file 2: Table S1. of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: List of differential expressed genes (at gene level) in early growth fruits. Expression values of the 1945 differentially expressed genes in early growth fruits identified by comparison of gene expression between two consecutive stages. Differentially expressed genes were identified by the Cuffdiff program with a cutoff of FDR &lt;0.05. (XLS 735Â kb)
DOI: 10.6084/m9.figshare.c.3631340_d4.v1
title: Additional file 4: Table S3. of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: List of differentially expressed genes (at isoform level) in early growth fruits. Expression values (FPKM) of the 1059 differentially expressed annotated transcripts in early growth fruits identified by comparison of gene expression between two consecutive stages. Genes with expression values of FPKM &gt; =1 and fold changes (FC) &gt; =4 were considered to be differentially expressed. (XLS 955 kb)
DOI: 10.6084/m9.figshare.c.3631340_d2.v1
title: Additional file 8: Table S5. of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: Primers used in this study. Description of data: information of primers used in this study. (XLS 38Â kb)
DOI: 10.6084/m9.figshare.c.3631340_d1.v1
title: Additional file 7: of Identification of alternative splicing events by RNA sequencing in early growth tomato fruits

description: AS landscape. The output from ASTALAVISTA analysis. All the AS events identified in this study. The data includes information on AS types and chromosomal coordinates. The file is tab-separated with one AS event per line, each one has following fields:     . TranscriptID1 and 2 are the two transcripts involved in a given AS event. For the exon-intron structure of the AS event, exon skipping (ES) is indicated by 1-2^,0, alternative donor (AD) by 1^,2^, alternative acceptor (AA) by 1-,2- and intron retention (IR) by 1^2-,0. More detailed description on the AS code (the exon-intron structure) can be found on the ASTALAVISTA webpage ( http://genome.crg.es/astalavista/FAQ.html ). (TXT 3580 kb)
DOI: 10.6084/m9.figshare.c.3631316_d1.v1
title: Additional File 1:

description: Table S1. Genes whose genomic alterations associated with drug sensitivity (Mannâ  Whitney U test, adjusted pÂ
DOI: 10.5281/zenodo.14504749
title: Top 300 Asian Universities - QS Rankings 2024

description: Top 300 Asian Universities - QS Rankings 2024

This dataset includes detailed rankings and performance metrics of the Top 300 Asian Universities based on the QS Rankings for 2024. The data provides rich insights into the academic, research, and international standing of leading institutions across Asia.

Dataset Features:

 

Rank & Ordinal Rank: Position of the university in the rankings.University Name: Name of the institution.Overall Score: Total score achieved based on QS methodology.City: City where the university is located.Country: Country where the university is located.

Performance Metrics:

Citations per Paper: Research impact based on citations.Papers per Faculty: Academic output per faculty member.Academic Reputation: Perception of academic quality.Employer Reputation: Recognition by employers globally.Faculty Student Ratio: Ratio of faculty to students.Staff with PhD: Percentage of academic staff holding a PhD.International Research Center: Collaboration in research centers.International Students: Proportion of students from abroad.Outbound Exchange & Inbound Exchange: Student exchange data.International Faculty: Proportion of faculty members from other countries.

Potential Uses of the Dataset:

- Analyze trends in academic and research performance of Asian universities.- Perform comparisons of universities across countries and regions.- Visualize metrics like research impact, faculty diversity, and international reputation.- Build machine learning models for ranking predictions or analysis.

Source:

QS Rankings 2024 (publicly available ranking data for educational purposes).
DOI: 10.5281/zenodo.14504750
title: Top 300 Asian Universities - QS Rankings 2024

description: Top 300 Asian Universities - QS Rankings 2024

This dataset includes detailed rankings and performance metrics of the Top 300 Asian Universities based on the QS Rankings for 2024. The data provides rich insights into the academic, research, and international standing of leading institutions across Asia.

Dataset Features:

 

Rank & Ordinal Rank: Position of the university in the rankings.University Name: Name of the institution.Overall Score: Total score achieved based on QS methodology.City: City where the university is located.Country: Country where the university is located.

Performance Metrics:

Citations per Paper: Research impact based on citations.Papers per Faculty: Academic output per faculty member.Academic Reputation: Perception of academic quality.Employer Reputation: Recognition by employers globally.Faculty Student Ratio: Ratio of faculty to students.Staff with PhD: Percentage of academic staff holding a PhD.International Research Center: Collaboration in research centers.International Students: Proportion of students from abroad.Outbound Exchange & Inbound Exchange: Student exchange data.International Faculty: Proportion of faculty members from other countries.

Potential Uses of the Dataset:

- Analyze trends in academic and research performance of Asian universities.- Perform comparisons of universities across countries and regions.- Visualize metrics like research impact, faculty diversity, and international reputation.- Build machine learning models for ranking predictions or analysis.

Source:

QS Rankings 2024 (publicly available ranking data for educational purposes).
DOI: 10.6084/m9.figshare.c.3629309_d1.v1
title: Additional file 1: Figure S1. of Impact of the front-of-pack 5-colour nutrition label (5-CNL) on the nutritional quality of purchases: an experimental study

description: (FSA score computation and 5-CNL attribution) and Supplemental material (communication leaflet). (ZIP 464 kb)
DOI: 10.6084/m9.figshare.c.3629240_d1.v1
title: Additional file 1: of Modulation of post-stroke degenerative and regenerative processes and subacute protection by site-targeted inhibition of the alternative pathway of complement

description: List of 248 genes and results of Nanostring mRNA expression analysis comparing wild-type control to CR2-fH-treated mice.
DOI: 10.6084/m9.figshare.c.3629222_d1.v1
title: Additional file 2: of Analysis of genetic differentiation and genomic variation to reveal potential regions of importance during maize improvement

description: Table S1. Summary information of sources and co-ancestral membership of 1857 accessions Table S2. GWAS results of flowering time related traits and relevant linked QTL or genes. Table S3. Genomic regions with DEST of more than 0.2 and relevant linked QTL. (XLSX 299 kb)
DOI: 10.6084/m9.figshare.c.3629222_d2.v1
title: Additional file 1: of Analysis of genetic differentiation and genomic variation to reveal potential regions of importance during maize improvement

description: Figure S1. Correlation analysis of the first five principal components (PCs) between specific subset with different marker size and the entire set with all markers. Figure S2. Pair-comparison of DEST on different chromosomes between the subpopulations. Figure S3. Summary statistic of flowering time related traits. Histograms show the distribution of flowering time related traits. Numbers in the up-right triangle are the Pearson coefficient between flowering time related traits and between environments for the same trait. The low-left triangle is the scatter plots between flowering time related traits and between environments for the same trait. Figure S4. Comparison of the genetic variation evaluated by using two datasets including 43,252 and 85 SNPs, respectively. Each scatter presents one accession. Black line is the fitted of all scatters. (ZIP 850 kb)
DOI: 10.6084/m9.figshare.c.3629159_d5.v1
title: Additional file 7: Table S3. of Complete depletion of primordial germ cells in an All-female fish leads to Sex-biased gene expression alteration and sterile All-male occurrence

description: DEGs between mature testis and ovary. (XLSX 3580Â kb)
DOI: 10.6084/m9.figshare.28001450.v12
title: GBM-Reservoir: Dataset and Segmentations

description: In this repository, we present a brain tumor database collection comprising 23,049 samples, with each sample including four different types of MRI brain scans: FLAIR, T1, T1ce, and T2. Additionally, two segmentation masks (ground truth) are provided for each sample. The first mask is the raw output from the registration process, while the second is a post-processed version of the first, designed to simplify interpretation and optimize it for network training. These samples have been acquired via registration process of 438 samples available at the moment of registration from the original dataset provided by the BraTS 2022 Challenge. Registering each pair of existing brain scans results in two additional scans that retain a similar brain shape while featuring varying tumor locations. Consequently, by registering all possible pairs, a dataset originally consisting of n samples can be expanded to n<sup>2</sup> samples. The original dataset was collected from different institutions under standard clinical conditions, but with different equipment and imaging protocols. As a result, the image quality is heterogeneous, reflecting the diversity of clinical practices across institutions. This dataset can be utilized for various tasks, such as developing fully automated segmentation algorithms for new, unseen brain tumor cases, particularly through deep learning-based approaches, since ground truth is provided for each sample.
DOI: 10.6084/m9.figshare.c.3628343_d2.v1
title: Additional file 2: of An ensemble framework for identifying essential proteins

description: Table S5-S6. The information of the top 100 proteins ranked by five centrality measures and by five ensemble methods on PIN24K. Table S7-S8. The information of the top 100 proteins ranked by five centrality measures and by five ensemble methods on PIN76K. (XLSX 64 kb)
DOI: 10.6084/m9.figshare.c.3628292_d9.v1
title: Additional file 1: of Insight into the AP2/ERF transcription factor superfamily in sesame and expression profiling of DREB subfamily under drought stress

description: Characteristics of sesame AP2/ERF Transcription factor gene family members. This table contains the type, distribution of AP2 domains, protein full length, subfamily, ORF length, number of CDS, lists of predicted domains. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.c.3628292_d6.v1
title: Additional file 9: of Insight into the AP2/ERF transcription factor superfamily in sesame and expression profiling of DREB subfamily under drought stress

description: List of primers used in quantitative real time- PCR analysis. (XLSX 12 kb)
DOI: 10.6084/m9.figshare.c.3628292_d1.v1
title: Additional file 4: of Insight into the AP2/ERF transcription factor superfamily in sesame and expression profiling of DREB subfamily under drought stress

description: Details of sesame AP2/ERF transcription factor- based SSR markers. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.c.3628229_d6.v1
title: MOESM4 of Genome-wide identification and evaluation of constitutive promoters in streptomycetes

description: Additional file 4: Dataset S3. Genes with highly stable expression profiles in different conditions generated by step-by-step analysis.
DOI: 10.6084/m9.figshare.c.3627203_d15.v1
title: Additional file 11: Table S8 of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: GiniClust clustering membership for Patel et al. study. (XLSX 42Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d12.v1
title: Additional file 14: Table S11. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: GiniClust clustering membership for Zeisel et al. study. (XLSX 76Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d11.v1
title: Additional file 8: Table S6. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Cluster 2 differentially expressed gene results for single-cell RNA-seq data from Klein et al. study. (XLSX 658Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d10.v1
title: Additional file 1: Table S1. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Gini index related statistics for the qPCR dataset from Guo et al. [9]. (XLSX 26Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d9.v1
title: Additional file 2: Table S2. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Cluster 5 differentially expressed gene results for qPCR data from Guo et al. study. (XLSX 92Â kb)
DOI: 10.15468/dl.jqt8j7
title: Occurrence Download

description: A dataset containing 16870 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-7.55679 35.86241,-0.19075 35.86241,-0.19075 39.66213,-7.55679 39.66213,-7.55679 35.86241))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Procambarus clarkii (Girard, 1852)"
 ]
}

 The dataset includes 16870 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037476-241126133413365/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.c.3627227_d1.v1
title: Additional file 1: of Genomic information of the arsenic-resistant bacterium Lysobacter arseniciresistens type strain ZS79T and comparison of Lysobacter draft genomes

description: Table S1. The proteins for Type II secretion in Lysobacter genomes. Table S2. The proteins for flagellar assembly in Lysobacter genomes. Table S3. The arsenic resistances genes found in five Lysobacter genomes. (XLSX 17Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d7.v1
title: Additional file 13: Table S10 of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Gini index related statistics for single-cell RNA-seq data from Zeisel et al study. (XLSX 999Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d4.v1
title: Additional file 6: Table S4. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Gini index related statistics for single-cell RNA-seq data from Klein et al. study. (XLSX 1435Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d5.v1
title: Additional file 12: Table S9 of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Cluster 3 differentially expressed gene results for single-cell RNA-seq data from Patel et al. study. (XLSX 212Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d3.v1
title: Additional file 3: Table S3. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: GiniClust clustering membership for Guo et al. study. (XLSX 37Â kb)
DOI: 10.6084/m9.figshare.c.3627194_d3.v1
title: Additional file 2: of CollapsABEL: an R library for detecting compound heterozygote alleles in genome-wide association studies

description: Source code of CollapsABEL the auxiliary package collUtils including documentation. (GZ 6805 kb)
DOI: 10.6084/m9.figshare.c.3627203_d2.v1
title: Additional file 16: Table12. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: Cluster 4 differentially expressed gene results for single-cell RNA-seq data from Zeisel et al. study. (XLSX 233Â kb)
DOI: 10.6084/m9.figshare.c.3627203_d1.v1
title: Additional file 7: Table S5. of GiniClust: detecting rare cell types from single-cell gene expression data with Gini index

description: GiniClust clustering membership for Klein et al. study. (XLSX 45Â kb)
DOI: 10.6084/m9.figshare.c.3626306_d2.v1
title: Additional file 14: Table S4. of Glucocorticoid receptor and nuclear factor kappa-b affect three-dimensional chromatin organization

description: Overview of restriction enzyme combinations and inverse PCR primer pairs used for 4C-seq. (XLS 31 kb)
DOI: 10.6084/m9.figshare.c.3626306_d1.v1
title: Additional file 15: Table S5. of Glucocorticoid receptor and nuclear factor kappa-b affect three-dimensional chromatin organization

description: Overview of sequenced and mapped reads of 4C-seq libraries. (XLS 31 kb)
DOI: 10.6084/m9.figshare.c.3626255_d9.v1
title: Additional file 4: of Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

description: Overexpressed proteins identified in invasive GBC cell lines. (XLS 406 kb)
DOI: 10.6084/m9.figshare.c.3626255_d7.v1
title: Additional file 5: of Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

description: Downregulated proteins identified in invasive GBC cell lines. (XLS 277 kb)
DOI: 10.6084/m9.figshare.c.3626240_d8.v1
title: Additional file 9: of Mesodermal ALK5 controls lung myofibroblast versus lipofibroblast cell fate

description: Raw data. (XLSX 247 kb)
DOI: 10.6084/m9.figshare.c.3626255_d6.v1
title: Additional file 3: of Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

description: Peptides identified in GBC cell lines using Sequest and Mascot search algorithm. (XLS 5659 kb)
DOI: 10.6084/m9.figshare.c.3626255_d2.v1
title: Additional file 2: of Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

description: Proteins identified in GBC cell lines using Sequest and Mascot search algorithm. (XLS 1180 kb)
DOI: 10.6084/m9.figshare.c.3626255_d1.v1
title: Additional file 1: of Macrophage migration inhibitory factor - a therapeutic target in gallbladder cancer

description: Properties of GBC cell lines used in the study. (XLS 27 kb)
DOI: 10.6084/m9.figshare.c.3625376_d4.v1
title: MOESM4 of Dissection of early transcriptional responses to water stress in Arundo donax L. by unigene-based RNA-seq

description: Additional file 4: Table S3. Stress-related A. donax DEGs.
DOI: 10.6084/m9.figshare.c.3625376_d3.v1
title: MOESM1 of Dissection of early transcriptional responses to water stress in Arundo donax L. by unigene-based RNA-seq

description: Additional file 1: Table S1. Experimental design, number of reads per library and sampling scheme.
DOI: 10.6084/m9.figshare.c.3625376_d2.v1
title: MOESM2 of Dissection of early transcriptional responses to water stress in Arundo donax L. by unigene-based RNA-seq

description: Additional file 2: Table S2. Validation of 10 A. donax DEGs by Real Time qrt-PCR.
DOI: 10.6084/m9.figshare.c.3625376_d1.v1
title: MOESM6 of Dissection of early transcriptional responses to water stress in Arundo donax L. by unigene-based RNA-seq

description: Additional file 6: Table S5. Slimmed GO terms overrepresented under different conditions.
DOI: 10.6084/m9.figshare.c.3625373_d1.v1
title: Additional file 4: of Mergeomics: multidimensional data integration to identify pathogenic perturbations to biological systems

description: Results from weighted key driver analysis for six cholesterol associated subnetworks of adipose and liver regulatory networks. (XLSX 402Â kb)
DOI: 10.6084/m9.figshare.c.3625346_d7.v1
title: Additional file 2: of WNT4 mediates estrogen receptor signaling and endocrine resistance in invasive lobular carcinoma cell lines

description: Sequence and product information for oligos used in this study. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.c.3625316_d1.v1
title: Additional file 1: of Neutrophil-lymphocyte ratio is associated with arterial stiffness in patients with peritoneal dialysis

description: Raw data of the study. (XLS 46 kb)
DOI: 10.6084/m9.figshare.c.3625322_d1.v1
title: MOESM1 of Tissue-independent and tissue-specific patterns of DNA methylation alteration in cancer

description: Additional file 1: Supplementary Table S1. Excel Table Spreadsheet listing details of the constitutively unmethylated CGIs (cu-CGIs), constitutively unmethylated gene promoters (cu-GPs) and constitutively methylated gene bodies (cm-GBs).
DOI: 10.15468/dl.2ugakv
title: Occurrence Download

description: A dataset containing 74 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stenocercus ochoai Fritts, 1972"
 ]
}

 The dataset includes 74 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037477-241126133413365/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.c.3624395_d4.v1
title: Additional file 1: Table S1. of Fine-mapping of qGW4.05, a major QTL for kernel weight and size in maize

description: The primers used in this study for fine-mapping qGW4.05 and candidate gene sequencing. (XLS 30 kb)
DOI: 10.6084/m9.figshare.c.3624356_d3.v1
title: Additional file 3 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping

description: Lung cancer gene signature. The full list of 322 genes (probesets) and their ranks for the optimal gene signature obtained from the lung cancer dataset GSE19804. (TAB 41.1 kb)
DOI: 10.6084/m9.figshare.c.3624356_d2.v1
title: Additional file 2 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping

description: Significant drugs returned with the k=50 HDACi gene signature. The list of significant drugs returned from sscMap using the k=50 HDACi gene signature from the â  simulated experimentâ  . All 7 known HDAC inhibitors were pulled out, but together with many other drugs, which may or may not related to HDAC inhibition. The main biological theme (HDAC inhibition) could potentially be weakned or even masked due to the co-existence of many other drugs. (XLSX 17 kb)
DOI: 10.6084/m9.figshare.c.3624356_d1.v1
title: Additional file 4 of A gene-signature progression approach to identifying candidate small-molecule cancer therapeutics with connectivity mapping

description: Breast cancer gene signature. The full list of 232 genes (probesets) and their ranks for the optimal gene signature obtained from the breast cancer dataset GSE15852. (TAB 29.9 kb)
DOI: 10.6084/m9.figshare.c.3624314_d1.v1
title: Additional file 2: of Production of Î˛-ionone by combined expression of carotenogenic and plant CCD1 genes in Saccharomyces cerevisiae

description: Primers used in the study.