cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11273246.v1
title: MOESM7 of Comparative genome analyses reveal the unique genetic composition and selection signals underlying the phenotypic characteristics of three Chinese domestic goat breeds

description: Additional file 7: Table S5. Summary of ROH detected in six goat populations (CB, JT, TC, MD, MN, and Bezoar ibex).
DOI: 10.25402/epi.17040443
title: Supplementary Tables – Alteration of m6A methylation landscape in a mouse model of scleroderma

description: <b>Supplementary Table 1.
 Primers used in qPCR and MeRIP-qPCR</b>
 <b>Supplementary
 Table 2. The quantification and quality of RNA in skin tissue of a mouse
 model of scleroderma<sup>*</sup></b>
 
 




<b>Supplementary
Table 3. The specific activity (pmol dye per μg cRNA) of the </b><b>labeled RNA</b>
DOI: 10.25402/epi.17040443.v1
title: Supplementary Tables – Alteration of m6A methylation landscape in a mouse model of scleroderma

description: <b>Supplementary Table 1.
 Primers used in qPCR and MeRIP-qPCR</b>
 <b>Supplementary
 Table 2. The quantification and quality of RNA in skin tissue of a mouse
 model of scleroderma<sup>*</sup></b>
 
 




<b>Supplementary
Table 3. The specific activity (pmol dye per μg cRNA) of the </b><b>labeled RNA</b>
DOI: 10.5281/zenodo.10663439
title: Crowding perception survey in the Metro system, Santiago, Chile

description: This is the data from a stated preference survey made in Chile, to Metro users in the city of Santiago. The objective is to analyse their perception of crowding in public transport.

 

If you use this database, please cite the following source:

 

Tirachini, A., Hurtubia, R., Dekker, T., Daziano, R.A. (2017) Estimation of crowding discomfort in public transport: Results from Santiago de Chile. Transportation Research Part A 103, 311–326. https://doi.org/10.1016/j.tra.2017.06.008
DOI: 10.5281/zenodo.10663440
title: Crowding perception survey in the Metro system, Santiago, Chile

description: This is the data from a stated preference survey made in Chile, to Metro users in the city of Santiago. The objective is to analyse their perception of crowding in public transport.

 

If you use this database, please cite the following source:

 

Tirachini, A., Hurtubia, R., Dekker, T., Daziano, R.A. (2017) Estimation of crowding discomfort in public transport: Results from Santiago de Chile. Transportation Research Part A 103, 311–326. https://doi.org/10.1016/j.tra.2017.06.008
DOI: 10.6084/m9.figshare.22807533
title: Additional file 2 of Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn’s disease: a multi-omics Mendelian randomization study

description: Additional file 2: Table S1. Characteristics of six transcriptome datasets used in the study. Table S2. 817 OS-related genes with a relevance score ≥ 7 obtained from GeneCards. Table S3. Meta-analysis of 708 differentially expressed OS genes from six datasets. Table S4. Cell type-specific expression analysis (CSEA) of 438 intestinal OS DEGs. Table S5. Summary-based Mendelian randomization (SMR) analysis from blood gene expression to CD (FDR &lt; 0.05). Table S6. SMR analysis from blood DNA methylation to CD (FDR &lt; 0.05). Table S7. SMR analysis from blood DNA methylation to gene expression (FDR &lt; 0.05). Table S8. Regulatory component annotation of 665 DNA methylation sites. Table S9. Meta-analysis between GTEx and 1000IBD intestinal cis-eQTLs (FDR &lt; 0.05). Table S10. SMR analysis from intestinal gene expression to CD (FDR &lt; 0.05). Table S11. Colocalization analysis between gene expression and gut microbiota (PPH4 &gt; 0.5). Table S12. Demographic characteristics of FAH-SYS IBD multi-omics cohort. Table S13. External cohort validation: intestinal DEG results between CD and HC. Table S14. External cohort validation: associations between intestinal gene expression and gut microbiota.
DOI: 10.6084/m9.figshare.22807533.v1
title: Additional file 2 of Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn’s disease: a multi-omics Mendelian randomization study

description: Additional file 2: Table S1. Characteristics of six transcriptome datasets used in the study. Table S2. 817 OS-related genes with a relevance score ≥ 7 obtained from GeneCards. Table S3. Meta-analysis of 708 differentially expressed OS genes from six datasets. Table S4. Cell type-specific expression analysis (CSEA) of 438 intestinal OS DEGs. Table S5. Summary-based Mendelian randomization (SMR) analysis from blood gene expression to CD (FDR &lt; 0.05). Table S6. SMR analysis from blood DNA methylation to CD (FDR &lt; 0.05). Table S7. SMR analysis from blood DNA methylation to gene expression (FDR &lt; 0.05). Table S8. Regulatory component annotation of 665 DNA methylation sites. Table S9. Meta-analysis between GTEx and 1000IBD intestinal cis-eQTLs (FDR &lt; 0.05). Table S10. SMR analysis from intestinal gene expression to CD (FDR &lt; 0.05). Table S11. Colocalization analysis between gene expression and gut microbiota (PPH4 &gt; 0.5). Table S12. Demographic characteristics of FAH-SYS IBD multi-omics cohort. Table S13. External cohort validation: intestinal DEG results between CD and HC. Table S14. External cohort validation: associations between intestinal gene expression and gut microbiota.
DOI: 10.6084/m9.figshare.22807617.v1
title: Additional file 7 of Single-cell RNA sequencing reveals the local cell landscape in mouse epididymal initial segment during aging

description: Additional file 7: Supplementary Table 1 List of the cell markers of cell tyes in the epididymal initial segment
DOI: 10.6084/m9.figshare.22807620
title: Additional file 8 of Single-cell RNA sequencing reveals the local cell landscape in mouse epididymal initial segment during aging

description: Additional file 8: Supplementary Table 2 Primers used for the quantitative real-time PCR
DOI: 10.6084/m9.figshare.22807620.v1
title: Additional file 8 of Single-cell RNA sequencing reveals the local cell landscape in mouse epididymal initial segment during aging

description: Additional file 8: Supplementary Table 2 Primers used for the quantitative real-time PCR
DOI: 10.6084/m9.figshare.22807617
title: Additional file 7 of Single-cell RNA sequencing reveals the local cell landscape in mouse epididymal initial segment during aging

description: Additional file 7: Supplementary Table 1 List of the cell markers of cell tyes in the epididymal initial segment
DOI: 10.6084/m9.figshare.20186293
title: Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

description: This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (<i>R</i><sup>2</sup>) was 0.9889, and the root means square error (RMSE) was 3.28 × 10<sup>−4</sup>, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.
DOI: 10.6084/m9.figshare.11797686
title: Additional file 3 of Comparative analysis of Lactobacillus gasseri from Chinese subjects reveals a new species-level taxa

description: Additional file3: Table S3. CRISPR-Cas systems in L. paragasseri and L. gasseri strains
DOI: 10.6084/m9.figshare.20186293.v1
title: Rapid detection of neutralising acid adulterants in raw milk using a milk component analyser and chemometrics

description: This study focused on the development of a method for the rapid detection of acid-neutralising adulterants in raw milk using a milk composition analyser. Qualitative analysis for the discrimination of different acid-neutralising acid adulterants in raw milk and quantification of NaSCN in adulterated raw milk were conducted, combined with chemometrics. The results showed that the milk component analyser combined with principal component analysis (PCA) could judge whether raw milk samples were adulterated but cannot identify the types of adulterated substances. Although partial least squares discrimination analysis (PLS-DA) can distinguish some adulterated raw milk samples, the accuracy rate was only 56.3%; the random forest (RF) model could recognise most adulterated raw milk samples with an accuracy rate of 97.5% and the F1-score was 0.9638. In the prediction model of NaSCN adulteration concentration in raw milk constructed by RF, the coefficient of determination (<i>R</i><sup>2</sup>) was 0.9889, and the root means square error (RMSE) was 3.28 × 10<sup>−4</sup>, suggesting a high prediction performance of the model. The effectiveness of the method for the detection of real samples in practical production was also proved. Based on the above results, it could conclude that the milk component analyser, combined with chemometrics, effectively distinguished acid-neutralising adulterants in raw milk. These findings provide a reference for the rapid detection of adulterants and the quality control of raw milk.
DOI: 10.6084/m9.figshare.11797686.v1
title: Additional file 3 of Comparative analysis of Lactobacillus gasseri from Chinese subjects reveals a new species-level taxa

description: Additional file3: Table S3. CRISPR-Cas systems in L. paragasseri and L. gasseri strains
DOI: 10.6084/m9.figshare.8127704
title: The contig order and arrangement in chromosome

description: The contig order and arrangement in chromosome.
DOI: 10.6084/m9.figshare.8127704.v1
title: The contig order and arrangement in chromosome

description: The contig order and arrangement in chromosome.
DOI: 10.6084/m9.figshare.7603442
title: L1 muts in MG.xlsx

description: minigenome assay to test the function of L1 mutants in viral RNA synthesis
DOI: 10.6084/m9.figshare.11273468
title: MOESM4 of Immune receptor repertoires in pediatric and adult acute myeloid leukemia

description: Additional file 4. Processed data for both AML and non-tumor samples.
DOI: 10.6084/m9.figshare.11273468.v1
title: MOESM4 of Immune receptor repertoires in pediatric and adult acute myeloid leukemia

description: Additional file 4. Processed data for both AML and non-tumor samples.
DOI: 10.6084/m9.figshare.11273474
title: MOESM1 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 1: Table S1. Clinical characteristics of patients in HCC tissue array cohort at the time of surgery (n = 101).
DOI: 10.6084/m9.figshare.11273474.v1
title: MOESM1 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 1: Table S1. Clinical characteristics of patients in HCC tissue array cohort at the time of surgery (n = 101).
DOI: 10.6084/m9.figshare.11273486
title: MOESM2 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 2: Table S2. Frequency of the abnormally expressed genes in HCC.
DOI: 10.6084/m9.figshare.11273486.v1
title: MOESM2 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 2: Table S2. Frequency of the abnormally expressed genes in HCC.
DOI: 10.6084/m9.figshare.11273495
title: MOESM3 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 3: Table S3. The publication history and the expression of the 37 high frequency genes in liver cancer and other malignant tumors.
DOI: 10.6084/m9.figshare.11273495.v1
title: MOESM3 of Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

description: Additional file 3: Table S3. The publication history and the expression of the 37 high frequency genes in liver cancer and other malignant tumors.
DOI: 10.6084/m9.figshare.25224092
title: Dataset for cnn4dos

description: This Item contains the dataset for my MPhil research titled <i>Descriptor-guided screening and understanding of 2D substrate supported single atomic CO</i><sub><em>2</em></sub><i>RR electrocatalysts</i>, available at https://eprints.qut.edu.au/241290/. Meanwhile most of the code is hosted at GitHub repo at: https://github.com/DanielYang59/cnn4dos.<br>
DOI: 10.6084/m9.figshare.25224092.v1
title: Dataset for cnn4dos

description: This Item contains the dataset for my MPhil research titled <i>Descriptor-guided screening and understanding of 2D substrate supported single atomic CO</i><sub><em>2</em></sub><i>RR electrocatalysts</i>, available at https://eprints.qut.edu.au/241290/. Meanwhile most of the code is hosted at GitHub repo at: https://github.com/DanielYang59/cnn4dos.<br>
DOI: 10.6084/m9.figshare.11273543
title: MOESM2 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 2: Table S1. Descriptive characteristics of 139 samples from gynecologic cancers used in the drug screening and/or the genomic evaluations.
DOI: 10.6084/m9.figshare.11273543.v1
title: MOESM2 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 2: Table S1. Descriptive characteristics of 139 samples from gynecologic cancers used in the drug screening and/or the genomic evaluations.
DOI: 10.6084/m9.figshare.11273552
title: MOESM3 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 3: Table S2. A list of the 37 drugs used in drug sensitivity analysis. The drugs were described by the chemical and/or generic names, their respective targets and clinical phases.
DOI: 10.6084/m9.figshare.11273552.v1
title: MOESM3 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 3: Table S2. A list of the 37 drugs used in drug sensitivity analysis. The drugs were described by the chemical and/or generic names, their respective targets and clinical phases.
DOI: 10.6084/m9.figshare.11273558
title: MOESM4 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 4: Table S3. Area under the curve (AUC) values of 37 drugs in 66 PDCs. NA represents the AUC value extracted from non-fitted dose response curve (DRC) that resulted in a non-convergent or ambiguous curve.
DOI: 10.6084/m9.figshare.11273558.v1
title: MOESM4 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 4: Table S3. Area under the curve (AUC) values of 37 drugs in 66 PDCs. NA represents the AUC value extracted from non-fitted dose response curve (DRC) that resulted in a non-convergent or ambiguous curve.
DOI: 10.6084/m9.figshare.11273564
title: MOESM5 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 5: Table S4. Tumor type-specific drug associations identified using 37-drug library. Wilcoxon rank sum test was applied to determine the relative differences of drug sensitivity between certain tumor type and all the other cancers.
DOI: 10.6084/m9.figshare.11273564.v1
title: MOESM5 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 5: Table S4. Tumor type-specific drug associations identified using 37-drug library. Wilcoxon rank sum test was applied to determine the relative differences of drug sensitivity between certain tumor type and all the other cancers.
DOI: 10.6084/m9.figshare.11273576
title: MOESM6 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 6: Table S5. The genomic profile of gynecologic tumor samples which was identified using CancerSCANTM sequencing and analysis protocol.
DOI: 10.6084/m9.figshare.11273576.v1
title: MOESM6 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 6: Table S5. The genomic profile of gynecologic tumor samples which was identified using CancerSCANTM sequencing and analysis protocol.
DOI: 10.6084/m9.figshare.11797869
title: Additional file 1 of Design of targeted primers based on 16S rRNA sequences in meta-transcriptomic datasets and identification of a novel taxonomic group in the Asgard archaea

description: Additional file 1: Table S1. Results of local blastn. Novel short-length 16S rRNA as query sequences and PacBio novel full-length 16S rRNA genes as subject sequences.
DOI: 10.6084/m9.figshare.11797869.v1
title: Additional file 1 of Design of targeted primers based on 16S rRNA sequences in meta-transcriptomic datasets and identification of a novel taxonomic group in the Asgard archaea

description: Additional file 1: Table S1. Results of local blastn. Novel short-length 16S rRNA as query sequences and PacBio novel full-length 16S rRNA genes as subject sequences.
DOI: 10.6084/m9.figshare.11273582
title: MOESM7 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 7: Table S6. Cell type-specific drug associations in EOCs. Wilcox rank sum test was applied to determine the relative differences of drug sensitivity between serous and clear cell type tumors.
DOI: 10.6084/m9.figshare.11273582.v1
title: MOESM7 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 7: Table S6. Cell type-specific drug associations in EOCs. Wilcox rank sum test was applied to determine the relative differences of drug sensitivity between serous and clear cell type tumors.
DOI: 10.6084/m9.figshare.11273591
title: MOESM2 of Single-molecule real-time sequencing facilitates the analysis of transcripts and splice isoforms of anthers in Chinese cabbage (Brassica rapa L. ssp. pekinensis)

description: Additional file 2 Table S1. Summary of polymerase reads from the PacBio Sequel platform. Table S2. Summary of subreads from the PacBio Sequel platform. Table S3. Summary of CCSs from the PacBio Sequel platform. Table S4. Global PID statistics before and after sequencing error correction. Table S5. Evaluation of full-length transcripts in the PacBio data set. Table S6. Classification of loci and isoforms mapped to the reference genome. Table S7. Functional annotation of all novel isoforms by the PacBio Sequel platform. Table S8. Information of lncRNAs from the PacBio Sequel platform. Table S9. ORF information detected by the PacBio Sequel platform. Table S10. Splice isoforms detected by PacBio Sequel platform. Table S11. Fusion genes detected by the PacBio Sequel platform. Table S12. Poly-A sites detected by the PacBio Sequel platform. Table S13. Anther and pollen development related genes in B. rapa genome v3.0. Table S14. Primers used for RT-PCR validation.
DOI: 10.6084/m9.figshare.11273591.v1
title: MOESM2 of Single-molecule real-time sequencing facilitates the analysis of transcripts and splice isoforms of anthers in Chinese cabbage (Brassica rapa L. ssp. pekinensis)

description: Additional file 2 Table S1. Summary of polymerase reads from the PacBio Sequel platform. Table S2. Summary of subreads from the PacBio Sequel platform. Table S3. Summary of CCSs from the PacBio Sequel platform. Table S4. Global PID statistics before and after sequencing error correction. Table S5. Evaluation of full-length transcripts in the PacBio data set. Table S6. Classification of loci and isoforms mapped to the reference genome. Table S7. Functional annotation of all novel isoforms by the PacBio Sequel platform. Table S8. Information of lncRNAs from the PacBio Sequel platform. Table S9. ORF information detected by the PacBio Sequel platform. Table S10. Splice isoforms detected by PacBio Sequel platform. Table S11. Fusion genes detected by the PacBio Sequel platform. Table S12. Poly-A sites detected by the PacBio Sequel platform. Table S13. Anther and pollen development related genes in B. rapa genome v3.0. Table S14. Primers used for RT-PCR validation.
DOI: 10.6084/m9.figshare.11273594
title: MOESM8 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 8: Table S7. Pharmacogenomic associations identified using integrative analysis of drug sensitivity results (AUC) and genomic alteration. The statistical significance was calculated using Wilcoxon rank sum test.
DOI: 10.6084/m9.figshare.11273594.v1
title: MOESM8 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 8: Table S7. Pharmacogenomic associations identified using integrative analysis of drug sensitivity results (AUC) and genomic alteration. The statistical significance was calculated using Wilcoxon rank sum test.
DOI: 10.6084/m9.figshare.11273597
title: MOESM9 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 9: Table S8. List of genes (n = 80) and sequencing protocol for CancerSCANTM, detecting cancer-driven variant.
DOI: 10.6084/m9.figshare.11273597.v1
title: MOESM9 of Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers

description: Additional file 9: Table S8. List of genes (n = 80) and sequencing protocol for CancerSCANTM, detecting cancer-driven variant.