cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3886/e139461v5
title: Messaging to boost COVID-19 vaccinations: emphasize personal health benefits

description: The rapid development of COVID-19 vaccines is a tremendous scientific response to the current global pandemic. However, vaccines per se do not save lives and restart economies. The success of the vaccines depends on the number of people getting vaccinated. We use a survey experiment to examine the impact on vaccine intentions of a variety of public health messages identified as particularly promising: three messages that emphasize different benefits from the vaccines (personal health, the health of others, and the recovery of local and national economies), and one message that emphasizes vaccine safety. Because people will likely be exposed to multiple messages in the real world, we also examine the effect of these messages in combinations. Based on a nationally quota representative sample of 3,048 adults in the United States, our findings suggest that several forms of public messages can increase vaccine intentions, but messaging that emphasizes personal health benefits had the largest impact.
DOI: 10.34740/kaggle/dsv/2304062
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.10299110
title: ESEC/FSE 20210, Authorship Attribution: IntelliJ IDEA dataset

description: A dataset of method creations/modifications/deletions in IntelliJ Community project. Metadata for all the changes is included.<br>An unpacked version of the dataset requires about 10GB memory.
DOI: 10.6084/m9.figshare.10299110.v1
title: ESEC/FSE 20210, Authorship Attribution: IntelliJ IDEA dataset

description: A dataset of method creations/modifications/deletions in IntelliJ Community project. Metadata for all the changes is included.<br>An unpacked version of the dataset requires about 10GB memory.
DOI: 10.6084/m9.figshare.14736858
title: H3K27ac P1_7dpi rep1 mm10

description: For UCSC
DOI: 10.17608/k6.auckland.14273654
title: Supplementary data 4. SNP functional annotation and a list of the pleiotropic SNPs identified between 18 AiDs

description: We identified that the AiD associated SNPs are located in the non-coding regions of the genome. Of those, 186 are pleiotropic SNPs (<i>i.e., </i>same loci associated with multiple AiDs) that were associated with more than two AiDs. Majority of them (N=60) are shared between Crohn's disease (CRD) and Ulcerative colitis (ULC). We have presented a list of pleiotropic SNPs and the associated diseases in Table 2. For disease abbreviations, check Supplementary data 1.
DOI: 10.17608/k6.auckland.14273654.v6
title: Supplementary data 4. SNP functional annotation and a list of the pleiotropic SNPs identified between 18 AiDs

description: We identified that the AiD associated SNPs are located in the non-coding regions of the genome. Of those, 186 are pleiotropic SNPs (<i>i.e., </i>same loci associated with multiple AiDs) that were associated with more than two AiDs. Majority of them (N=60) are shared between Crohn's disease (CRD) and Ulcerative colitis (ULC). We have presented a list of pleiotropic SNPs and the associated diseases in Table 2. For disease abbreviations, check Supplementary data 1.
DOI: 10.17608/k6.auckland.14273654.v5
title: Supplementary data 4. SNP functional annotation and a list of the pleiotropic SNPs identified between 18 AiDs

description: We identified that the AiD associated SNPs are enriched in the regulatory regions of the genome. Of those, 186 are pleiotropic SNPs (<i>i.e., </i>same loci associated with multiple AiDs) that were associated with more than two AiDs. Majority of them (N=60) are shared between Crohn's disease (CRD) and Ulcerative colitis (ULC). We have presented a list of pleiotropic SNPs and the associated diseases in Table 2. For disease abbreviations, check Supplementary data 1.
DOI: 10.6084/m9.figshare.14736861.v1
title: H3K27ac P1_3dpi rep1 mm10

description: For UCSC
DOI: 10.6084/m9.figshare.14736861
title: H3K27ac P1_3dpi rep1 mm10

description: For UCSC
DOI: 10.6084/m9.figshare.14736864.v1
title: Comparison of neutrophil to lymphocyte ratio and prognostic nutritional index with other clinical and molecular biomarkers for prediction of glioblastoma multiforme outcome

description: Objective:Pre- and post-operative neutrophil to lymphocyte ratio (NLR) and prognostic nutritional index (PNI) and other prognostic clinicopathological variables were correlated with progression free survival (PFS) and overall survival (OS) of Glioblastoma Multiforme (GBM) patients. <br>Methods:GBM patients (n=87, single-centre, recruited 2013-2019) were retrospectively divided into low and high groups using literature-derived cut-offs (NLR = 5.07, PNI = 46.97). Kaplan-Meier survival curves and log rank tests assessed PFS and OS. Univariate and multivariate analyses identified PFS and OS prognosticators. <br>Results:High vs low post-operative PNI cohort was associated with longer PFS (279 vs 136 days, p=0.009), but significance was lost on multivariate analysis. Post-operative ECOG (p=0.043), daily dexamethasone (p=0.023) and IDH mutation (p=0.046) were significant on multivariate analysis for PFS. High pre- and post-operative PNI were associated with improved OS (384 vs 114 days, p=0.034 and 516 vs 245 days, p=0.001, respectively). Low postoperative NLR correlated with OS (408 vs 429 days, p=0.029). On multivariate analysis using forward selection process, extent of resection (EOR) (GTR vs biopsy, p=0.004 and STR vs biopsy, p=0.011), and any previous surgery (p=0.014) were independent prognostic biomarkers for OS. On multivariate analysis of these latter variables with literature-derived prognostic biomarkers, EOR remained significantly associated with OS (p=0.037). <br>Conclusion:EOR, followed by having any surgery prior to GBM, are the most significant independent predictors of GBM patient's OS. Post-operative ECOG, daily dexamethasone and IDH mutations are independent prognostic biomarkers for PFS. PNI may be superior to NLR. Post- vs pre-operative serum inflammatory marker levels may be associated with survival.
DOI: 10.6084/m9.figshare.14736864
title: Comparison of neutrophil to lymphocyte ratio and prognostic nutritional index with other clinical and molecular biomarkers for prediction of glioblastoma multiforme outcome

description: Objective:Pre- and post-operative neutrophil to lymphocyte ratio (NLR) and prognostic nutritional index (PNI) and other prognostic clinicopathological variables were correlated with progression free survival (PFS) and overall survival (OS) of Glioblastoma Multiforme (GBM) patients. <br>Methods:GBM patients (n=87, single-centre, recruited 2013-2019) were retrospectively divided into low and high groups using literature-derived cut-offs (NLR = 5.07, PNI = 46.97). Kaplan-Meier survival curves and log rank tests assessed PFS and OS. Univariate and multivariate analyses identified PFS and OS prognosticators. <br>Results:High vs low post-operative PNI cohort was associated with longer PFS (279 vs 136 days, p=0.009), but significance was lost on multivariate analysis. Post-operative ECOG (p=0.043), daily dexamethasone (p=0.023) and IDH mutation (p=0.046) were significant on multivariate analysis for PFS. High pre- and post-operative PNI were associated with improved OS (384 vs 114 days, p=0.034 and 516 vs 245 days, p=0.001, respectively). Low postoperative NLR correlated with OS (408 vs 429 days, p=0.029). On multivariate analysis using forward selection process, extent of resection (EOR) (GTR vs biopsy, p=0.004 and STR vs biopsy, p=0.011), and any previous surgery (p=0.014) were independent prognostic biomarkers for OS. On multivariate analysis of these latter variables with literature-derived prognostic biomarkers, EOR remained significantly associated with OS (p=0.037). <br>Conclusion:EOR, followed by having any surgery prior to GBM, are the most significant independent predictors of GBM patient's OS. Post-operative ECOG, daily dexamethasone and IDH mutations are independent prognostic biomarkers for PFS. PNI may be superior to NLR. Post- vs pre-operative serum inflammatory marker levels may be associated with survival.
DOI: 10.6084/m9.figshare.14736858.v1
title: H3K27ac P1_7dpi rep1 mm10

description: For UCSC
DOI: 10.5519/qd.moutadeq
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (4805209 records)
DOI: 10.21227/3wae-r624
title: Nondestructive Analysis of Internal Quality in Pears with Self-made Near Infrared Spectrum Detector Combined with Multivariate Data Processing

description: Taste index (TI) is the ratio of soluble solid content (SSC) to titratable acidity (TA). This index can be used to determine the taste and ripening stage of pears and characterize their flavor. The objective of this paper was to use a portable near-infrared (NIR) spectrometer and multivariate data analysis algorithms for the nondestructive detection of the TI of pears. For calculation convenience, a global numerical transformation of the TI value was formulated. The optimal partial least squares regression (PLSR) model was based on the new value of TI (nVTI) at a TA percentage (PTA) of 90% and a TI percentage (PTI) of 10%, and the model exhibited good performance. The PLSR model based on full variables without pretreatment had the best results. However, the full-spectrum PLSR model contained 397 variables, and the large number of variables led to overfitting of calibration models; therefore, it was necessary to select effective wavelengths from full wavelengths. As for the TI property measurement, a genetic algorithm (GA) was used to select the characteristic wavelengths for measuring TI based on the NIR spectrum obtained by an NIR spectrometer. The number of useful variables selected by the GA was 81. The numbers of characteristic variables selected by the GA-SPA, GA-MCARS, and GA-BOSS were 19, 16, and 22, respectively. The PLSR models based on the variables selected by the GA-BOSS had the smallest number of partial least squares components, demonstrating the good performance of the model. The Rc, RMSECV, Rp, RMSEP, and RPD of the PLSR model based on the useful variables selected by the GA-BOSS method were 0.971, 0.074%, 0.983, 0.066%, and 4.91, respectively. The results show that the portable NIR spectrometer with multivariate data processing is a good tool for the rapid and nondestructive analysis of TI in pears.
DOI: 10.6084/m9.figshare.14737259.v1
title: Additional file 9 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 9: Table S7. PIGNON results on HER2+/TN expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737256.v1
title: Additional file 8 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 8: Table S6. PIGNON results on HER2+/HR+ expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737253.v1
title: Additional file 7 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 7: Table S5. PIGNON results on HR+/TN expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737250.v1
title: Additional file 6 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 6: Table S4. PIGNON results on unweighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737247.v1
title: Additional file 5 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 5: Table S3. PIGNON results on unweighted BioGRID network with unweighted sampling
DOI: 10.6084/m9.figshare.14737244.v1
title: Additional file 4 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 4: Table S2. PIGNON results on HER2+/TN expression-weighted BioGRID network with unweighted sampling
DOI: 10.6084/m9.figshare.14737241.v1
title: Additional file 3 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 3: Table S1. PIGNON results on HER2+/TN expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737235.v1
title: Additional file 28 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 28: File S9. Protein clusters identified by Markov Clustering Algorithm with &gt;=3 proteins in breast cancer subtype expression-weighted BioGRID networks
DOI: 10.6084/m9.figshare.14737230.v1
title: Additional file 8 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 8 Details and metadata of RNAseq dataset downloaded from ENCODE for k-mer based quantification of candidates in diversified types of cells.
DOI: 10.6084/m9.figshare.14737220.v1
title: Additional file 23 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 23: File S8. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HER2+/HR+ expression-weighted BioGRID network
DOI: 10.6084/m9.figshare.14737218.v1
title: Additional file 4 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 4 All information needed about selected Mlincs (differentially expressed and selected by Boruta feature selection).
DOI: 10.6084/m9.figshare.14737217.v1
title: Additional file 22 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 22: File S7. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HR+/TN expression-weighted BioGRID network
DOI: 10.6084/m9.figshare.14737214.v1
title: Additional file 21 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 21: File S6. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HER2+/TN expression-weighted BioGRID network
DOI: 10.6084/m9.figshare.14737212.v1
title: Additional file 2 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 2 List of differentially expressed annotated genes between “MSC” and “non MSC” groups.
DOI: 10.6084/m9.figshare.14737211.v1
title: Additional file 20 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 20: File S5. NEA results on Tyanova et al.’s dataset using the BioGRID network
DOI: 10.6084/m9.figshare.14737209.v1
title: Additional file 1 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 1 Details and metadata of RNAseq dataset(s) downloaded for unannotated lncRNA prediction and differential expression between “MSC” and “non-MSC” groups.
DOI: 10.6084/m9.figshare.14737206.v1
title: Additional file 18 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 18 Details and metadata of RNAseq dataset downloaded for k-mer based quantification of candidates in MSCs in different biological situations.
DOI: 10.6084/m9.figshare.14737205.v1
title: Additional file 19 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 19: File S4. GSEA results on Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737202.v1
title: Additional file 18 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 18: File S3. Ontologizer results of significantly dysregulated proteins with loosened-up cut-offs from t-test results in Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737200.v1
title: Additional file 16 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 16 Single-cell RNAseq data of Adipose MSCs by X. Liu et al, used for co-expression research at the cell level.
DOI: 10.6084/m9.figshare.14737199.v1
title: Additional file 17 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 17: File S2. Ontologizer results of significantly dysregulated proteins from t-test results in Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737197.v1
title: Additional file 15 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 15 Details and metadata of FANTOM6 dataset used for functional prediction.
DOI: 10.6084/m9.figshare.14737196.v1
title: Additional file 16 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 16: File S1. t-test results on protein expression values from Tyanova et al.’s dataset
DOI: 10.6084/m9.figshare.14737191.v1
title: Additional file 14 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 14 Potential interactions between selected Mlincs and annotated proteins, found by LncADeep.
DOI: 10.6084/m9.figshare.14737184.v1
title: Additional file 12 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 12: Table S10. PIGNON results on unweighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737181.v1
title: Additional file 11 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 11: Table S9. PIGNON results on HR+/TN expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737178.v1
title: Additional file 10 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 10: Table S8. PIGNON results on HER2+/HR+ expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737110.v1
title: Additional file 4 of Low awareness and common misconceptions about schistosomiasis in endemic lowland areas in Western Ethiopia: a mixed-methods study

description: Additional file 4: Supplementary file 4 Data.xlsx
DOI: 10.6084/m9.figshare.14737091.v1
title: Additional file 1 of Long-term results of topical 0.02% tacrolimus ointment for refractory ocular surface inflammation in pediatric patients

description: Additional file 1. Raw data from pediatric patients using topical 0.02% tacrolimus ointment for the treatment of refractory ocular surface inflammation.
DOI: 10.6084/m9.figshare.14737086.v1
title: Additional file 1 of A copper alloy light cannon from Grodno: an example of early firearms from Eastern Europe

description: Additional file 1. Chemistry of late medieval and modern period copper alloy European firearms.
DOI: 10.6084/m9.figshare.14737062.v1
title: Additional file 4 of Effect of herbal extracts on peripheral nerve regeneration after microsurgery of the sciatic nerve in rats

description: Additional file 4: Figure S3. The liquid chromatography-mass spectrometry (LCMS) chromatograph of Aconitum carmichaelii Debeaux extracts.
DOI: 10.6084/m9.figshare.14737059.v1
title: Additional file 3 of Effect of herbal extracts on peripheral nerve regeneration after microsurgery of the sciatic nerve in rats

description: Additional file 3: Figure S2. The liquid chromatography-mass spectrometry (LCMS) chromatograph of Coptis japonica (Thunb.) Makino extracts.
DOI: 10.6084/m9.figshare.14736996.v1
title: Additional file 7 of Tsunami evacuation times and routes to safe zones: a GIS-based approach to tsunami evacuation planning on the island of Stromboli, Italy

description: Additional file 7: Appendix G. Evaluation of the possibility of evacuating in less than 4 min in Piscità (a) Evacuation of all the centroids in the Piscità zone. (b) Specific evacuation of the centroids to the RAEP1. (c) Individual evacuation of 101 centroid to the RAEP1.
DOI: 10.6084/m9.figshare.14736954.v1
title: Additional file 2 of Germline nuclear-predominant Pten murine model exhibits impaired social and perseverative behavior, microglial activation, and increased oxytocinergic activity

description: Additional file 2. contains the raw differential expression data from transcriptomic surgery of P40 Y68H cortex.
DOI: 10.6084/m9.figshare.14736924.v1
title: Additional file 2 of Annotation of snoRNA abundance across human tissues reveals complex snoRNA-host gene relationships

description: Additional file 2. Supplementary table S5 describes the abundance across tissues and characteristics of all expressed snoRNAs and their host gene.
DOI: 10.6084/m9.figshare.14737259
title: Additional file 9 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 9: Table S7. PIGNON results on HER2+/TN expression-weighted STRING network with weighted sampling
DOI: 10.6084/m9.figshare.14737256
title: Additional file 8 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 8: Table S6. PIGNON results on HER2+/HR+ expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737253
title: Additional file 7 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 7: Table S5. PIGNON results on HR+/TN expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737250
title: Additional file 6 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 6: Table S4. PIGNON results on unweighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737247
title: Additional file 5 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 5: Table S3. PIGNON results on unweighted BioGRID network with unweighted sampling
DOI: 10.6084/m9.figshare.14737244
title: Additional file 4 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 4: Table S2. PIGNON results on HER2+/TN expression-weighted BioGRID network with unweighted sampling
DOI: 10.6084/m9.figshare.14737241
title: Additional file 3 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional file 3: Table S1. PIGNON results on HER2+/TN expression-weighted BioGRID network with weighted sampling
DOI: 10.6084/m9.figshare.14737235
title: Additional file 28 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 28: File S9. Protein clusters identified by Markov Clustering Algorithm with &gt;=3 proteins in breast cancer subtype expression-weighted BioGRID networks
DOI: 10.6084/m9.figshare.14737230
title: Additional file 8 of Long non-coding RNA exploration for mesenchymal stem cell characterisation

description: Additional file 8 Details and metadata of RNAseq dataset downloaded from ENCODE for k-mer based quantification of candidates in diversified types of cells.
DOI: 10.6084/m9.figshare.14737220
title: Additional file 23 of PIGNON: a protein–protein interaction-guided functional enrichment analysis for quantitative proteomics

description: Additional File 23: File S8. Ontologizer results on clusters identified by MCL with &gt;= 3 proteins in the HER2+/HR+ expression-weighted BioGRID network