cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8222108.v1
title: Additional file 4: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S1. Number of TCGA patients contributing to this study across 32 cancer types. (XLSX 42 kb)
DOI: 10.6084/m9.figshare.8222090
title: Additional file 3: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Descriptive statistics. (CSV 423 bytes)
DOI: 10.6084/m9.figshare.8222090.v1
title: Additional file 3: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Descriptive statistics. (CSV 423 bytes)
DOI: 10.6084/m9.figshare.8222075
title: Additional file 1: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Raw data. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222081
title: Additional file 2: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Data DOI. (TXT 163 bytes)
DOI: 10.6084/m9.figshare.8222078
title: Additional file 16: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S13. Number of CCLE cell lines contributing to this study across 19 cancer types. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222081.v1
title: Additional file 2: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Data DOI. (TXT 163 bytes)
DOI: 10.6084/m9.figshare.8222075.v1
title: Additional file 1: of Correlation between early clinical exposure environment, attitudes toward basic medicine, and medical studentsâ basic science learning performance

description: Raw data. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222069
title: Additional file 15: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S12. PC-MA scores of genes in reactive stroma signature. (XLSX 58 kb)
DOI: 10.6084/m9.figshare.8222078.v1
title: Additional file 16: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S13. Number of CCLE cell lines contributing to this study across 19 cancer types. (XLSX 21 kb)
DOI: 10.6084/m9.figshare.8222069.v1
title: Additional file 15: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S12. PC-MA scores of genes in reactive stroma signature. (XLSX 58 kb)
DOI: 10.6084/m9.figshare.8222057
title: Additional file 14: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S11. Frequency of module-associated lncRNA dysregulation in cancer. (a) LncRNAs differential expressed in each cancer. (b) LncRNAs differentially expressed in at least one cancer type and their dysregulation classification. (c) Number and proportion of each dysregulation class in each module. (XLSX 78 kb)
DOI: 10.6084/m9.figshare.8222054
title: Additional file 13: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S10. Module-specific gene lists of extracellular-associated modules. (a) ME16-specific. (b) ME12- specific. (XLSX 70 kb)
DOI: 10.6084/m9.figshare.8222054.v1
title: Additional file 13: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S10. Module-specific gene lists of extracellular-associated modules. (a) ME16-specific. (b) ME12- specific. (XLSX 70 kb)
DOI: 10.6084/m9.figshare.8222057.v1
title: Additional file 14: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S11. Frequency of module-associated lncRNA dysregulation in cancer. (a) LncRNAs differential expressed in each cancer. (b) LncRNAs differentially expressed in at least one cancer type and their dysregulation classification. (c) Number and proportion of each dysregulation class in each module. (XLSX 78 kb)
DOI: 10.6084/m9.figshare.8222048
title: MOESM2 of Scrutinizing the triad of Vibrio tapetis, the skin barrier and pigmentation as determining factors in the development of skin ulcerations in wild common dab (Limanda limanda)

description: Additional file 2. Scoring of the different parameters that are part of the â Gross Ulceration Scoreâ (GUS). The GUS is the sum of the score of all the parameters indicated below and represents the severity of an ulceration.
DOI: 10.6084/m9.figshare.8222045
title: Additional file 12: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S9. LncRNA detection in pre-clinical tumour models. (a) Assessment of expression levels of each lncRNA in cell line and PDX tumour models. (b) Number and proportion of lncRNAs detected in cell lines/PDX models in each module. (XLSX 166 kb)
DOI: 10.6084/m9.figshare.8222048.v1
title: MOESM2 of Scrutinizing the triad of Vibrio tapetis, the skin barrier and pigmentation as determining factors in the development of skin ulcerations in wild common dab (Limanda limanda)

description: Additional file 2. Scoring of the different parameters that are part of the â Gross Ulceration Scoreâ (GUS). The GUS is the sum of the score of all the parameters indicated below and represents the severity of an ulceration.
DOI: 10.6084/m9.figshare.8222045.v1
title: Additional file 12: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S9. LncRNA detection in pre-clinical tumour models. (a) Assessment of expression levels of each lncRNA in cell line and PDX tumour models. (b) Number and proportion of lncRNAs detected in cell lines/PDX models in each module. (XLSX 166 kb)
DOI: 10.6084/m9.figshare.8222033
title: Additional file 11: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S8. Number and percentage lncRNAs in each module with ChipSeq evidence of SMAD3 occupancy. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8222030
title: Additional file 10: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S7. Enrichment of AP1 transcription factor binding sites in protein coding genes achieving PC-MA in module 7. (XLSX 31 kb)
DOI: 10.6084/m9.figshare.8222033.v1
title: Additional file 11: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S8. Number and percentage lncRNAs in each module with ChipSeq evidence of SMAD3 occupancy. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.8222030.v1
title: Additional file 10: of Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

description: Table S7. Enrichment of AP1 transcription factor binding sites in protein coding genes achieving PC-MA in module 7. (XLSX 31 kb)
DOI: 10.6084/m9.figshare.8222021
title: Additional file 3: of NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

description: Table S2. Full results on the synthetic benchmark, shown are the Spearman correlation values between the estimated OG abundances and the distribution being simulated. (XLSX 6 kb)
DOI: 10.6084/m9.figshare.8222015
title: Additional file 2: of NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

description: Table S1. Complete timing information for benchmark on real data for NGLess, MOCAT2, and bwa/htseq-count. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.8222015.v1
title: Additional file 2: of NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

description: Table S1. Complete timing information for benchmark on real data for NGLess, MOCAT2, and bwa/htseq-count. (XLSX 11 kb)
DOI: 10.6084/m9.figshare.8222021.v1
title: Additional file 3: of NG-meta-profiler: fast processing of metagenomes using NGLess, a domain-specific language

description: Table S2. Full results on the synthetic benchmark, shown are the Spearman correlation values between the estimated OG abundances and the distribution being simulated. (XLSX 6 kb)
DOI: 10.6084/m9.figshare.8222000
title: MOESM1 of Global serum proteomic changes in water buffaloes infected with Fasciola gigantica

description: Additional file 1: Table S1. Differentially expressed proteins identified in the present study.
DOI: 10.6084/m9.figshare.8222000.v1
title: MOESM1 of Global serum proteomic changes in water buffaloes infected with Fasciola gigantica

description: Additional file 1: Table S1. Differentially expressed proteins identified in the present study.
DOI: 10.6084/m9.figshare.8220938
title: The character of multidrug-resistant methicillin-resistant Staphylococcus aureus (MRSA) isolated from a subtropical river and its tributaries contaminated by the nearby livestock industry

description: The Methicillin-resistant<i> Staphylococcus aureus</i> (MRSA) is a major health threat that can cause infections in hospitals, communities, and animal husbandry areas. The subtropical river basin of the Puzih River in Taiwan is the confluence of river branches flowing through agricultural settlements containing farmlands and animal husbandry areas. A typical LA-MRSA is SCCmec IV or V, either of which lacks the Panton–Valentine leukocidin (PVL) gene but has broad variable patterns of antimicrobial susceptibility. This study aimed to determine the existence of MRSA in the Puzih River and to investigate multidrug resistance and SCCmec typing to explore possible routes of transmission. Our results revealed that the detection rate (7.8%) for MRSA in the suburban livestock intensive areas was higher than for other Puzih River areas. The ratio for multidrug-resistant (MDR) MRSA against all isolates was 76.9% (30/39). Major MDR isolates (66.7%) exhibited resistance to chloramphenicol, ciprofloxacin, clindamycin, erythromycin, sulfamethoxazole-trimethoprim, and tetracycline. Moreover, diversity in MDR patterns was higher in MRSA isolates from downtown and suburban livestock intensive areas than in other Puzih River areas. The majority of MRSA isolates (64.1%) in this study possessed SCCmec type IV without the existence of PVL, which is a typical characteristic of LA-MRSA. The enterobacterial repetitive intergenic consensus PCR (ERIC-PCR) typing method provided a method to distinguish SCCmec types of MRSA isolates and was advantageous for discriminating the diversity of resistance patterns. This study thus highlights the potential threat posed by highly drug-resistant LA-MRSA to human health through waterborne transmission.<br>
DOI: 10.6084/m9.figshare.8220938.v1
title: The character of multidrug-resistant methicillin-resistant Staphylococcus aureus (MRSA) isolated from a subtropical river and its tributaries contaminated by the nearby livestock industry

description: The Methicillin-resistant<i> Staphylococcus aureus</i> (MRSA) is a major health threat that can cause infections in hospitals, communities, and animal husbandry areas. The subtropical river basin of the Puzih River in Taiwan is the confluence of river branches flowing through agricultural settlements containing farmlands and animal husbandry areas. A typical LA-MRSA is SCCmec IV or V, either of which lacks the Panton–Valentine leukocidin (PVL) gene but has broad variable patterns of antimicrobial susceptibility. This study aimed to determine the existence of MRSA in the Puzih River and to investigate multidrug resistance and SCCmec typing to explore possible routes of transmission. Our results revealed that the detection rate (7.8%) for MRSA in the suburban livestock intensive areas was higher than for other Puzih River areas. The ratio for multidrug-resistant (MDR) MRSA against all isolates was 76.9% (30/39). Major MDR isolates (66.7%) exhibited resistance to chloramphenicol, ciprofloxacin, clindamycin, erythromycin, sulfamethoxazole-trimethoprim, and tetracycline. Moreover, diversity in MDR patterns was higher in MRSA isolates from downtown and suburban livestock intensive areas than in other Puzih River areas. The majority of MRSA isolates (64.1%) in this study possessed SCCmec type IV without the existence of PVL, which is a typical characteristic of LA-MRSA. The enterobacterial repetitive intergenic consensus PCR (ERIC-PCR) typing method provided a method to distinguish SCCmec types of MRSA isolates and was advantageous for discriminating the diversity of resistance patterns. This study thus highlights the potential threat posed by highly drug-resistant LA-MRSA to human health through waterborne transmission.<br>
DOI: 10.6084/m9.figshare.8220929
title: Oliver thesis - Primers.xlsx

description: List of primers used in Oliver Mantovani's thesis, including sequence and function
DOI: 10.6084/m9.figshare.8220929.v1
title: Oliver thesis - Primers.xlsx

description: List of primers used in Oliver Mantovani's thesis, including sequence and function
DOI: 10.6084/m9.figshare.8220896
title: 190520_BIS29_experiment.xlsx

description: Dataset of BIS29 experiment. Expression of double membrane transporters in cyanobacteria for increased terpenoid extrusion
DOI: 10.6084/m9.figshare.8220896.v1
title: 190520_BIS29_experiment.xlsx

description: Dataset of BIS29 experiment. Expression of double membrane transporters in cyanobacteria for increased terpenoid extrusion
DOI: 10.6084/m9.figshare.8220869
title: CCDB_7290.jpg

description: Maximum intensity projection through a 3D volume of mouse brain assembled from mosaics obtained through 16 optical sections stained for fluoromyelin(green), DiI(red), and TO-PRO3(blue) which stain for myelin, blood vessels, and cells, respectively.
DOI: 10.6084/m9.figshare.8220869.v1
title: CCDB_7290.jpg

description: Maximum intensity projection through a 3D volume of mouse brain assembled from mosaics obtained through 16 optical sections stained for fluoromyelin(green), DiI(red), and TO-PRO3(blue) which stain for myelin, blood vessels, and cells, respectively.
DOI: 10.6084/m9.figshare.8220866
title: MacKenzie_etal_(2019)_Master_Data.xlsx

description: Raw Data and Statistical Analysis as required as part of manuscript submission for PLoS.
DOI: 10.6084/m9.figshare.8220833
title: CCDB_7266.jpg

description: Large scale mosaic of a 40 um thick parasagittal section of rat brain immunolabeled with against the plasma membrane calcium pump protein, PMCA1a. using a polyclonal antibody against the C terminus and peroxidase-DAB as the chromagen. The zoomified image for browsing on the web is slightly downsampled from the full resolution data available for download.
DOI: 10.6084/m9.figshare.8220866.v1
title: MacKenzie_etal_(2019)_Master_Data.xlsx

description: Raw Data and Statistical Analysis as required as part of manuscript submission for PLoS.
DOI: 10.6084/m9.figshare.8220827
title: nonlinear-25-grams-number

description: 25-grams
DOI: 10.6084/m9.figshare.8220833.v1
title: CCDB_7266.jpg

description: Large scale mosaic of a 40 um thick parasagittal section of rat brain immunolabeled with against the plasma membrane calcium pump protein, PMCA1a. using a polyclonal antibody against the C terminus and peroxidase-DAB as the chromagen. The zoomified image for browsing on the web is slightly downsampled from the full resolution data available for download.
DOI: 10.6084/m9.figshare.8220824
title: nonlinear-25-grams

description: 25-grams
DOI: 10.6084/m9.figshare.8220827.v1
title: nonlinear-25-grams-number

description: 25-grams
DOI: 10.6084/m9.figshare.8220818
title: nonlinear-24-grams-number

description: 24-grams
DOI: 10.6084/m9.figshare.8220818.v1
title: nonlinear-24-grams-number

description: 24-grams
DOI: 10.6084/m9.figshare.8220806
title: nonlinear-23-grams

description: 232-grams
DOI: 10.6084/m9.figshare.8220809
title: nonlinear-23-grams-number

description: 23-grams
DOI: 10.6084/m9.figshare.8220815
title: nonlinear-24-grams

description: 24-grams
DOI: 10.6084/m9.figshare.8220809.v1
title: nonlinear-23-grams-number

description: 23-grams
DOI: 10.6084/m9.figshare.8220806.v1
title: nonlinear-23-grams

description: 232-grams
DOI: 10.6084/m9.figshare.8220764
title: nonlinear-22-grams-number

description: 22-grams
DOI: 10.6084/m9.figshare.8220815.v1
title: nonlinear-24-grams

description: 24-grams
DOI: 10.6084/m9.figshare.8220752
title: nonlinear-22-grams

description: 22-grams
DOI: 10.6084/m9.figshare.8220764.v1
title: nonlinear-22-grams-number

description: 22-grams
DOI: 10.6084/m9.figshare.8220749
title: COPIL Annie example

description: Example dataset from COPIL<br>
DOI: 10.6084/m9.figshare.8220746
title: COPIL Buxton example

description: Example COPIL data for an instruction session<br>
DOI: 10.6084/m9.figshare.8220749.v1
title: COPIL Annie example

description: Example dataset from COPIL<br>
DOI: 10.6084/m9.figshare.8220752.v1
title: nonlinear-22-grams

description: 22-grams
DOI: 10.6084/m9.figshare.8220494
title: Cross_Reference_HMMs_Info.txt

description: Supplemental text file from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220512
title: Species_Phylogeny_construction

description: Data from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220494.v1
title: Cross_Reference_HMMs_Info.txt

description: Supplemental text file from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220470
title: dataset cleaned

description: Dataset used.<br>
DOI: 10.6084/m9.figshare.8220482
title: metadata.xlsx

description: metadata from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220512.v1
title: Species_Phylogeny_construction

description: Data from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220467
title: COG_clustering.proteinortho

description: Clustering of dataset from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220482.v1
title: metadata.xlsx

description: metadata from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220470.v1
title: dataset cleaned

description: Dataset used.<br>
DOI: 10.6084/m9.figshare.8220284
title: nonlinear-21-grams-number

description: 21-grams
DOI: 10.6084/m9.figshare.8220467.v1
title: COG_clustering.proteinortho

description: Clustering of dataset from Getz_et_al_2019
DOI: 10.6084/m9.figshare.8220284.v1
title: nonlinear-21-grams-number

description: 21-grams
DOI: 10.6084/m9.figshare.8220275
title: nonlinear-20-grams-number

description: 20-grams
DOI: 10.6084/m9.figshare.8220281
title: nonlinear-21-grams

description: 21-grams
DOI: 10.6084/m9.figshare.8220272
title: nonlinear-20-grams

description: 20-grams
DOI: 10.6084/m9.figshare.8220281.v1
title: nonlinear-21-grams

description: 21-grams
DOI: 10.6084/m9.figshare.8220269
title: Data Digestive tract H ingens

description: Raw data of the paper submitted to Aquaculture Research.
DOI: 10.6084/m9.figshare.8220269.v1
title: Data Digestive tract H ingens

description: Raw data of the paper submitted to Aquaculture Research.
DOI: 10.6084/m9.figshare.8220272.v1
title: nonlinear-20-grams

description: 20-grams
DOI: 10.6084/m9.figshare.8220275.v1
title: nonlinear-20-grams-number

description: 20-grams
DOI: 10.6084/m9.figshare.8219486
title: nonlinear-19-grams

description: 19-grams
DOI: 10.6084/m9.figshare.8219606
title: nonlinear-19-grams-number

description: 19-grams
DOI: 10.6084/m9.figshare.8219606.v1
title: nonlinear-19-grams-number

description: 19-grams
DOI: 10.6084/m9.figshare.8219486.v1
title: nonlinear-19-grams

description: 19-grams
DOI: 10.6084/m9.figshare.8219054
title: nonlinear-18-grams-number

description: 18-grams
DOI: 10.6084/m9.figshare.8219051
title: nonlinear-18-grams

description: 18-grams
DOI: 10.6084/m9.figshare.8219054.v1
title: nonlinear-18-grams-number

description: 18-grams
DOI: 10.6084/m9.figshare.8219879.v2
title: MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments

description: Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of <i>Andropogon gerardii</i>, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.8219045
title: nonlinear-17-grams-number

description: 17-grams
DOI: 10.6084/m9.figshare.8219051.v1
title: nonlinear-18-grams

description: 18-grams
DOI: 10.6084/m9.figshare.8219042
title: nonlinear-17-grams

description: 17-grams
DOI: 10.6084/m9.figshare.8219045.v1
title: nonlinear-17-grams-number

description: 17-grams
DOI: 10.6084/m9.figshare.8219039
title: Fasta file of barcode sequences

description: Fasta file containing the 572 barcode sequences used in statistical analyses. <br><br>Corresponding scientific paper: Boggs, L.M.; Scheible, M.K.; Machado, G.; Meiklejohn, K.A. Single Fragment or Bulk Soil DNA Metabarcoding: Which is Better for Characterizing Biological Taxa Found in Surface Soils for Sample Separation? <i>Genes</i> <b>2019</b>, <i>10</i>, 431<br>
DOI: 10.6084/m9.figshare.8219042.v1
title: nonlinear-17-grams

description: 17-grams