cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14059827
title: Additional file 9 of Genome-wide association of rice response to blast fungus identifies loci for robust resistance under high nitrogen

description: Additional file 9. QTLs detected from GWAS of Blast Susceptibility with nitrogen interaction. Each QTL is given with the traits that led to its identification, the number of significant (−log(P) ≥ 5) and sub-significant (4 ≤−log(P)
DOI: 10.6084/m9.figshare.14059827.v1
title: Additional file 9 of Genome-wide association of rice response to blast fungus identifies loci for robust resistance under high nitrogen

description: Additional file 9. QTLs detected from GWAS of Blast Susceptibility with nitrogen interaction. Each QTL is given with the traits that led to its identification, the number of significant (−log(P) ≥ 5) and sub-significant (4 ≤−log(P)
DOI: 10.6084/m9.figshare.14059851
title: Additional file 1 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 1: Table S1. The data sample uploaded to the platform.
DOI: 10.6084/m9.figshare.14059851.v1
title: Additional file 1 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 1: Table S1. The data sample uploaded to the platform.
DOI: 10.6084/m9.figshare.14059854
title: Additional file 2 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 2: Table S2. The information of the topological properties of a part of nodes.
DOI: 10.6084/m9.figshare.14059854.v1
title: Additional file 2 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 2: Table S2. The information of the topological properties of a part of nodes.
DOI: 10.6084/m9.figshare.14059857
title: Additional file 3 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 3: Table S3. The basic information of modules.
DOI: 10.6084/m9.figshare.14059857.v1
title: Additional file 3 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 3: Table S3. The basic information of modules.
DOI: 10.6084/m9.figshare.14059860
title: Additional file 4 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 4: Table S4. The enrichment terms of Module50.
DOI: 10.6084/m9.figshare.14059860.v1
title: Additional file 4 of CeNet Omnibus: an R/Shiny application to the construction and analysis of competing endogenous RNA network

description: Additional file 4: Table S4. The enrichment terms of Module50.
DOI: 10.6084/m9.figshare.14059938
title: Additional file 1 of Clinical outcomes of bulky parotid gland cancers: need for self-examination and screening program for early diagnosis of parotid tumors

description: Additional file 1: Supplementary File Raw data for this study
DOI: 10.6084/m9.figshare.14059938.v1
title: Additional file 1 of Clinical outcomes of bulky parotid gland cancers: need for self-examination and screening program for early diagnosis of parotid tumors

description: Additional file 1: Supplementary File Raw data for this study
DOI: 10.6084/m9.figshare.14059949
title: Additional file 2 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 2: Table S2. All tissues used in the study. Detailed description of all tissues samples used in the study, including the unique identifiers for three histone ChIP-seq libraries, the corresponding input libraries and RNA-seq libraries sequenced from each individual.
DOI: 10.6084/m9.figshare.14059949.v1
title: Additional file 2 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 2: Table S2. All tissues used in the study. Detailed description of all tissues samples used in the study, including the unique identifiers for three histone ChIP-seq libraries, the corresponding input libraries and RNA-seq libraries sequenced from each individual.
DOI: 10.6084/m9.figshare.14059952
title: Additional file 3 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 3: Table S3. ChIP-seq mapping statistics. The number of reads sequenced, mapped, passing quality control and after duplicate removal for all ChIP-seq and input libraries used in this study.
DOI: 10.6084/m9.figshare.14059955
title: Additional file 4 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 4: Table S5. RNA-seq mapping statistics. The number of sequenced, mapped, and multimapping RNA-seq reads for all libraries used in this study.
DOI: 10.6084/m9.figshare.14059955.v1
title: Additional file 4 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 4: Table S5. RNA-seq mapping statistics. The number of sequenced, mapped, and multimapping RNA-seq reads for all libraries used in this study.
DOI: 10.6084/m9.figshare.14059952.v1
title: Additional file 3 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 3: Table S3. ChIP-seq mapping statistics. The number of reads sequenced, mapped, passing quality control and after duplicate removal for all ChIP-seq and input libraries used in this study.
DOI: 10.6084/m9.figshare.14059958
title: Additional file 5 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 5: Table S6. Recently evolved regulatory regions in transposable elements. Counts of recently evolved regulatory regions overlapping different repeat groups use for enrichment analyses in Fig. 5a.
DOI: 10.6084/m9.figshare.14059961
title: Additional file 6 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 6: Table S7. Maintained regulatory regions in transposable elements. Counts of maintained regulatory regions overlapping different repeat groups used for enrichment analyses in Figure S10A.
DOI: 10.6084/m9.figshare.14059964
title: Additional file 7 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 7: Table S8. Multimapping reads. The percentage of quality controlled reads, i.e. MAPQ &gt; 1 and duplicate removed, that map uniquely within non-repetitive genomic regions and LINE L1 and L2 genomic regions.
DOI: 10.6084/m9.figshare.14059958.v1
title: Additional file 5 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 5: Table S6. Recently evolved regulatory regions in transposable elements. Counts of recently evolved regulatory regions overlapping different repeat groups use for enrichment analyses in Fig. 5a.
DOI: 10.6084/m9.figshare.14059961.v1
title: Additional file 6 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 6: Table S7. Maintained regulatory regions in transposable elements. Counts of maintained regulatory regions overlapping different repeat groups used for enrichment analyses in Figure S10A.
DOI: 10.6084/m9.figshare.14059964.v1
title: Additional file 7 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 7: Table S8. Multimapping reads. The percentage of quality controlled reads, i.e. MAPQ &gt; 1 and duplicate removed, that map uniquely within non-repetitive genomic regions and LINE L1 and L2 genomic regions.
DOI: 10.6084/m9.figshare.14059967
title: Additional file 8 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 8: Table S9. Evolutionarily dynamic regulatory regions in transposable elements. Counts of evolutionarily dynamic regulatory regions overlapping different repeat groups use for enrichment analyses in Fig. 5e.
DOI: 10.6084/m9.figshare.14059967.v1
title: Additional file 8 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 8: Table S9. Evolutionarily dynamic regulatory regions in transposable elements. Counts of evolutionarily dynamic regulatory regions overlapping different repeat groups use for enrichment analyses in Fig. 5e.
DOI: 10.6084/m9.figshare.14059970.v1
title: Additional file 9 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 9: Data S1. The standalone ZIP file containing custom scripts and pipelines produced for this study.
DOI: 10.6084/m9.figshare.14059970
title: Additional file 9 of LINE retrotransposons characterize mammalian tissue-specific and evolutionarily dynamic regulatory regions

description: Additional file 9: Data S1. The standalone ZIP file containing custom scripts and pipelines produced for this study.
DOI: 10.6084/m9.figshare.14059971
title: Additional file 1 of A hierarchical machine learning framework for the analysis of large scale animal movement data

description: Additional file 1 Figure S1: The simulated seasonal migration. (A) The mean location of the migration as a function of time. Note, there is a seasonal to-and-fro migration with a gradually shifting Northern range. (B) An example trajectory for a single year. The outbound and inbound movements of a single individual are shown in red and blue respectively. Figure S2: Convergence diagnostics for sampler. Potential scale reduction factor for 4 separate runs of 500 samples. (A-B) Raw whitened variables for the lengthscale (red) and amplitude (blue) (C-D) Transformed variables after Cholesky transformation for lengthscale (red) and amplitude (blue). Potential scale reduction factor for the observation noise was computed as 1.005. Figure S3: Effective sample size for 2000 MCMC samples from 4 chains. Figure S4: Posterior distribution of observation error related to the GPS locations. Figure S5: Posterior mean and 95% credible intervals of raw latent functions (prior to exponential transformation). Low value regions have high uncertainty as any movements in the trajectory in these periods are attributed to observation error. (A) Log correlation length. (B) Log velocity amplitude. Figure S6: Step lengths and turn angles from simulated and empirical data. Data was divided into sequences of 2-hour sections (A-B) and 4-hour sections (C-D). The distribution of lengths of segments and turn angles between segments is shown.
DOI: 10.6084/m9.figshare.14059971.v1
title: Additional file 1 of A hierarchical machine learning framework for the analysis of large scale animal movement data

description: Additional file 1 Figure S1: The simulated seasonal migration. (A) The mean location of the migration as a function of time. Note, there is a seasonal to-and-fro migration with a gradually shifting Northern range. (B) An example trajectory for a single year. The outbound and inbound movements of a single individual are shown in red and blue respectively. Figure S2: Convergence diagnostics for sampler. Potential scale reduction factor for 4 separate runs of 500 samples. (A-B) Raw whitened variables for the lengthscale (red) and amplitude (blue) (C-D) Transformed variables after Cholesky transformation for lengthscale (red) and amplitude (blue). Potential scale reduction factor for the observation noise was computed as 1.005. Figure S3: Effective sample size for 2000 MCMC samples from 4 chains. Figure S4: Posterior distribution of observation error related to the GPS locations. Figure S5: Posterior mean and 95% credible intervals of raw latent functions (prior to exponential transformation). Low value regions have high uncertainty as any movements in the trajectory in these periods are attributed to observation error. (A) Log correlation length. (B) Log velocity amplitude. Figure S6: Step lengths and turn angles from simulated and empirical data. Data was divided into sequences of 2-hour sections (A-B) and 4-hour sections (C-D). The distribution of lengths of segments and turn angles between segments is shown.
DOI: 10.21227/4j65-j106
title: Appendix Data

description: Appendix date for manuscript
DOI: 10.6084/m9.figshare.14060027
title: Additional file 1 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 1: Table S1. Sample information and ARGs in Enterococcus; The same background color represents the resistance genes corresponding to the same class of antibiotics.
DOI: 10.6084/m9.figshare.14060027.v1
title: Additional file 1 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 1: Table S1. Sample information and ARGs in Enterococcus; The same background color represents the resistance genes corresponding to the same class of antibiotics.
DOI: 10.6084/m9.figshare.14060030
title: Additional file 2 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 2: Table S2. GIs and phages in Enterococcus; The yellow background represents GIs and prophages located in the same contig; The blue fonts represents GIs and plasmids located in the same contig.
DOI: 10.6084/m9.figshare.14060030.v1
title: Additional file 2 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 2: Table S2. GIs and phages in Enterococcus; The yellow background represents GIs and prophages located in the same contig; The blue fonts represents GIs and plasmids located in the same contig.
DOI: 10.6084/m9.figshare.14060033
title: Additional file 3 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 3: Table S3. Spearman’s correlation of ARGs in Enterococcus; ** represents p
DOI: 10.6084/m9.figshare.14060036
title: Additional file 4 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 4: Table S4. Spearman’s correlation between ARGs and GI exchange frequency.
DOI: 10.6084/m9.figshare.14060036.v1
title: Additional file 4 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 4: Table S4. Spearman’s correlation between ARGs and GI exchange frequency.
DOI: 10.6084/m9.figshare.14060033.v1
title: Additional file 3 of Genomic islands mediate environmental adaptation and the spread of antibiotic resistance in multiresistant Enterococci - evidence from genomic sequences

description: Additional file 3: Table S3. Spearman’s correlation of ARGs in Enterococcus; ** represents p
DOI: 10.6084/m9.figshare.14060037
title: Additional file 1 of Distinct mechanisms of resistance to fulvestrant treatment dictate level of ER independence and selective response to CDK inhibitors in metastatic breast cancer

description: Additional file 1. Table showing normalized gene expression data from parental and fulvestrant-resistant CAMA-1, MCF7, T47D and HCC1428 cell lines.
DOI: 10.6084/m9.figshare.14060037.v1
title: Additional file 1 of Distinct mechanisms of resistance to fulvestrant treatment dictate level of ER independence and selective response to CDK inhibitors in metastatic breast cancer

description: Additional file 1. Table showing normalized gene expression data from parental and fulvestrant-resistant CAMA-1, MCF7, T47D and HCC1428 cell lines.
DOI: 10.6084/m9.figshare.14060063
title: Additional file 1 of A simple method to estimate the in-house limit of detection for genetic mutations with low allele frequencies in whole-exome sequencing analysis by next-generation sequencing

description: Additional file 1: Figure S1. Limit of detection (LOD) of allele frequency in whole-exome sequencing (WES) analysis. Three or five point-moving average curve plots of percentage relative standard deviation (%RSD) against the mean vales of WES-allele frequencies (AFs) were used. The %RSD values were plotted against the mean WES-AFs. The consistent trend in %RSD vs. mean WES-AFs is represented by the 3 or 5 point-moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following sequencing data sizes: 5 (A), 15 (B), 30 (C), and 40 (D) Gbp. Figure S2. AF LOD in WES analysis using the Illumina TruSeq Exome Enrichment Kit for library preparation. The Illumina TruSeq Exome Enrichment Kit was used to capture the exome region, and downstream analysis was performed using a workflow designed by Illumina, Inc. The %RSD values of AFs calculated from quadruplicate technical replicates were plotted against the mean values of AFs (mean WES-AFs) obtained from quadruplicate technical replicates. The consistent trend in %RSD vs. mean WES-AFs is represented by the moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following WES data sizes: 15 (A), 30 (B), and 40 (C) Gbp. (Note: the 5 Gbp WES data size was excluded from this analysis because the on-target rates at some low AF positions were very low.) (D) Line graph showing the trend in correlation between LOD and sequencing data size (from A–C). When WES was performed using a sequencing data size &gt; 15 Gbp, the LOD was relatively constant and in the range of 5–10%. Table S1. Summary of sequencing quality (sequencing data size: approximately 5 – 40 Gbp). Table S2. Sequencing results. Supplementary Methods. Exome sequencing via the Illumina exome capture platform. Exome enrichment was independently performed with quadruplicate technical replicates using the TruSeq Exome Enrichment Kit (Illumina). After enriched exome libraries were multiplexed, the libraries were sequenced using a NextSeq 500 sequencing platform (Illumina) according to manufacturer’s instructions. We used the FASTQ Toolkit App in BaseSpace™ Sequence Hub designed by Illumina, Inc. to filter the data for quality and read length. Alignment to reference sequences and variant identification were performed with the Enrichment App (Illumina). The sequence data from this experiment has been deposited on the NCBI (BioProject accession number PRJNA670243).
DOI: 10.6084/m9.figshare.14060063.v1
title: Additional file 1 of A simple method to estimate the in-house limit of detection for genetic mutations with low allele frequencies in whole-exome sequencing analysis by next-generation sequencing

description: Additional file 1: Figure S1. Limit of detection (LOD) of allele frequency in whole-exome sequencing (WES) analysis. Three or five point-moving average curve plots of percentage relative standard deviation (%RSD) against the mean vales of WES-allele frequencies (AFs) were used. The %RSD values were plotted against the mean WES-AFs. The consistent trend in %RSD vs. mean WES-AFs is represented by the 3 or 5 point-moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following sequencing data sizes: 5 (A), 15 (B), 30 (C), and 40 (D) Gbp. Figure S2. AF LOD in WES analysis using the Illumina TruSeq Exome Enrichment Kit for library preparation. The Illumina TruSeq Exome Enrichment Kit was used to capture the exome region, and downstream analysis was performed using a workflow designed by Illumina, Inc. The %RSD values of AFs calculated from quadruplicate technical replicates were plotted against the mean values of AFs (mean WES-AFs) obtained from quadruplicate technical replicates. The consistent trend in %RSD vs. mean WES-AFs is represented by the moving average curve on the graph. The derivation of LOD30%RSD is illustrated by dotted lines (see arrow). All analyses were performed using the following WES data sizes: 15 (A), 30 (B), and 40 (C) Gbp. (Note: the 5 Gbp WES data size was excluded from this analysis because the on-target rates at some low AF positions were very low.) (D) Line graph showing the trend in correlation between LOD and sequencing data size (from A–C). When WES was performed using a sequencing data size &gt; 15 Gbp, the LOD was relatively constant and in the range of 5–10%. Table S1. Summary of sequencing quality (sequencing data size: approximately 5 – 40 Gbp). Table S2. Sequencing results. Supplementary Methods. Exome sequencing via the Illumina exome capture platform. Exome enrichment was independently performed with quadruplicate technical replicates using the TruSeq Exome Enrichment Kit (Illumina). After enriched exome libraries were multiplexed, the libraries were sequenced using a NextSeq 500 sequencing platform (Illumina) according to manufacturer’s instructions. We used the FASTQ Toolkit App in BaseSpace™ Sequence Hub designed by Illumina, Inc. to filter the data for quality and read length. Alignment to reference sequences and variant identification were performed with the Enrichment App (Illumina). The sequence data from this experiment has been deposited on the NCBI (BioProject accession number PRJNA670243).
DOI: 10.6084/m9.figshare.14060079
title: Additional file 1 of Global transcriptome profile of the developmental principles of in vitro iPSC-to-motor neuron differentiation

description: Additional file 1. List of differentially expressed genes (DEG) at each stage of MN development that were found to be significantly (p
DOI: 10.6084/m9.figshare.14060079.v1
title: Additional file 1 of Global transcriptome profile of the developmental principles of in vitro iPSC-to-motor neuron differentiation

description: Additional file 1. List of differentially expressed genes (DEG) at each stage of MN development that were found to be significantly (p
DOI: 10.6084/m9.figshare.14060084
title: Additional file 2 of The application of next-generation sequence-based DNA barcoding for bloodmeal detection in host-seeking wild-caught Ixodes scapularis nymphs

description: Additional file 2. Consolidated sequence results for all wild-caught nymphs. Consolidated sequence identifications generated after the bioinformatic pipeline for each wild-caught nymph including both PCR replicates.
DOI: 10.6084/m9.figshare.14060084.v1
title: Additional file 2 of The application of next-generation sequence-based DNA barcoding for bloodmeal detection in host-seeking wild-caught Ixodes scapularis nymphs

description: Additional file 2. Consolidated sequence results for all wild-caught nymphs. Consolidated sequence identifications generated after the bioinformatic pipeline for each wild-caught nymph including both PCR replicates.
DOI: 10.6084/m9.figshare.14060115
title: Additional file 2 of Amplicon-based skin microbiome profiles collected by tape stripping with different adhesive film dressings: a comparative study

description: Additional file 2: Table S1. Amplicon sequence variant (ASV)-level comparison between three different adhesives. ASVs that shared more than 0.1% on average are listed.