cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23729/3160254e-85e9-4268-a636-5b3e54091706
title: CAN bus dataset collected from a heavy-duty truck

description: This dataset contains over 180 hours of CAN bus traffic collected from a Renault Euro VI heavy-duty truck (Renault T520 6X2) over several driving sessions (with different drivers) in varying traffic conditions (e.g. urban, rural). Data is collected from the FMS standard CAN interface operating at 250 kbit/s via a PCAN-USB adapter, and read using python-can library with SocketCAN as the CAN Interface Module. Recorded CAN frames conform to the SAE J1939 standard. All collected frames are data frames (i.e. there are no remote frames). CAN bus data is recorded as comma separated values in .csv files. Each row corresponds to a single CAN frame, and contains timestamp, CAN identifier, data length, and data. Each file contains fifty thousand CAN frames, which correspond to approximately 1 minute of recording.
DOI: 10.6084/m9.figshare.16540356
title: Table S9 (expanded): list including all fine mapped cardiac GWAS loci

description: This R object includes a list of all 210 fine mapped loci. Each element includes: · gene or transcript ID; · phenotype (rna or isoform); · eQTL type (0 = primary, 1-5 = conditional); · trait ID from the pan UK BioBank; · locus coordinates; · a data frame with the GWAS summary statistics for the locus, including: position; variant ID, effect size (meta-analysis); standard error and p-value; · a data frame with the eQTL summary statistics, as in Table S2: chromosome, position, reference, alternative, RS ID, variant ID, allele frequency, effect size (beta) and its standard error, p-value, gene or transcript ID, Bonferroni-adjusted p-value (across all tests for the same gene/transcript) and type (0 = primary, 1-5 = conditional); · a data frame with the fine mapped variants, sorted by PPA: variant ID; PPA (calculated using the coloc.abf function), position, cumulative PPA, and credible set (TRUE if the variant is included in the 99% credible set) · a vector with the variant IDs of all the variants included in the 99% credible set; · the credible set size
DOI: 10.6084/m9.figshare.16540353
title: Table S3 and S6 (expanded): Full PPAs between each pair of eGenes or eIsoforms

description: This R object includes a list of three elements, each corresponding to the colocalization results between: 1) eIsoform vs. eIsoform; 2) eGene vs. eGene; and 3) eIsoform vs. eGene. Each element is a data frame with the following information: tested variants, posterior probabilities of association (PPA) 0-4; gene or transcript IDs; eQTL types (0 = primary; 1-5 = conditional); variant ID of the variant with highest PPA, allele frequency, chromosome, position reference allele, alternative allele, p-values for both eQTLs; and PPA for the variant.
DOI: 10.18119/n9188w
title: Multi-Scale Integrated Cell (MuSIC) v1
DOI: 10.6084/m9.figshare.14983857.v1
title: Table S2 - Ecological Modeling Results

description: Table displaying the contribution of assembly processes towards assembly in individual wells and different well groupings.
DOI: 10.48366/r141336
title: Nitrogen uptake rates in the Arabian Sea and the Bay of Bengal, the Indian Ocean

description: This comparative analysis compares the Nitrogen uptake (i.e., NO3, NH4, urea or total Nitrogen) rates during the different Indian monsoon seasons in the Arabian Sea and the Bay of Bengal basins of the Indian Ocean.
DOI: 10.5281/zenodo.5347925
title: Subset of AmazonQA annotated with answerability and semantic & syntactic embedding

description: 3755 Instances taken from the review-question dataset AmazonQA. The file data_answerability contain the 3755 instances annotated with answerability, answer tag (aligned with the passage) and answer text. The file data_annotated contains 1818 answerable instances, annotated with embedding constructions in the answer text. Inventory_phrases and inventory_simple_words contain the expressions of logical operator, implicative and factive predicates that can be used in embedding annotation. annotate.py is the script for embedding annotation.
DOI: 10.5281/zenodo.5347924
title: Subset of AmazonQA annotated with answerability and semantic & syntactic embedding

description: 3755 Instances taken from the review-question dataset AmazonQA. The file data_answerability contain the 3755 instances annotated with answerability, answer tag (aligned with the passage) and answer text. The file data_annotated contains 1818 answerable instances, annotated with embedding constructions in the answer text. Inventory_phrases and inventory_simple_words contain the expressions of logical operator, implicative and factive predicates that can be used in embedding annotation. annotate.py is the script for embedding annotation.
DOI: 10.25345/c5wr86
title: MassIVE MSV000088038 - Pah1 phosphorylation by Rim11
title: Pah1 phosphorylation by Rim11
DOI: 10.15468/guwwss
title: Phylogeny of families in the Pectinoidea (Mollusca: Bivalvia): importance of the fossil record

description: This dataset contains the digitized treatments in Plazi based on the original journal article Waller, Thomas R. (2006): Phylogeny of families in the Pectinoidea (Mollusca: Bivalvia): importance of the fossil record. Zoological Journal of the Linnean Society 148 (3): 313-342, DOI: 10.1111/j.1096-3642.2006.00258.x, URL: https://academic.oup.com/zoolinnean/article-lookup/doi/10.1111/j.1096-3642.2006.00258.x
DOI: 10.34740/kaggle/dsv/2576080
title: Vietnam SARS-CoV-2 | COVID-19 Data
DOI: 10.25387/g3.15183750
title: Supplemental Material for de Souza et al., 2021

description: Supplementary File S1. Imputed genotype of Brandes × Wray sweet sorghum recombinant inbred lines for physical map-based analyses. (TXT)Supplementary File S2. Non-imputed genotype of Brandes × Wray sweet sorghum recombinant inbred lines for genetic map-based analyses. (TXT)<br>Supplementary File S3. Phenotypic adjusted means for Brandes × Wray sweet sorghum recombinant inbred lines from each trial (T1, T2, T3). (XLSX)<br>Supplementary File S4. Marker distances from each chromosome of the genetic map for Brandes × Wray sweet sorghum recombinant inbred lines. (XLSX)<br>Supplementary File S5. Heatmaps of LOD scores (above diagonal) and recombination fractions (below diagonal) between pairs of markers from each chromosome of the genetic map for Brandes × Wray sweet sorghum recombinant inbred lines. (PDF)<br>Supplementary File S6. Physical map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines using a mapping window size of 100 kbp. (XLSX)<br>Supplementary File S7. Physical map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines using a mapping window size of 200 kbp. (PDF)<br>Supplementary File S8. Genetic map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines. (XLSX)<br>Supplementary File S9. Co-located QTLs across multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines based on physical and genetic maps. (XLSX)<br>Supplementary File S10. List of putative genes within a 10 kbp-window on each side of QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match Sorghum bicolor genome v3.1.1. (XLSX)<br>Supplementary File S11. List of 150 differentially expressed genes from sweet sorghum associated with QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match Sorghum bicolor genome v3.1.1. (XLSX)<br>Supplementary File S12. List of 50 known sucrose-related associated with QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match <i>Sorghum bicolor</i> genome v3.1.1. (XLSX)<br>Supplementary File S13. List of previously reported QTLs according to Sorghum QTL Atlas (https://aussorgm.org.au/sorghum-qtl-atlas/). (XLSX)
DOI: 10.25387/g3.15183750.v1
title: Supplemental Material for de Souza et al., 2021

description: Supplementary File S1. Imputed genotype of Brandes × Wray sweet sorghum recombinant inbred lines for physical map-based analyses. (TXT)Supplementary File S2. Non-imputed genotype of Brandes × Wray sweet sorghum recombinant inbred lines for genetic map-based analyses. (TXT)<br>Supplementary File S3. Phenotypic adjusted means for Brandes × Wray sweet sorghum recombinant inbred lines from each trial (T1, T2, T3). (XLSX)<br>Supplementary File S4. Marker distances from each chromosome of the genetic map for Brandes × Wray sweet sorghum recombinant inbred lines. (XLSX)<br>Supplementary File S5. Heatmaps of LOD scores (above diagonal) and recombination fractions (below diagonal) between pairs of markers from each chromosome of the genetic map for Brandes × Wray sweet sorghum recombinant inbred lines. (PDF)<br>Supplementary File S6. Physical map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines using a mapping window size of 100 kbp. (XLSX)<br>Supplementary File S7. Physical map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines using a mapping window size of 200 kbp. (PDF)<br>Supplementary File S8. Genetic map-based multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines. (XLSX)<br>Supplementary File S9. Co-located QTLs across multiple interval mapping for individual (T1, T2, T3) and multiple trials (MT) for Brandes × Wray sweet sorghum recombinant inbred lines based on physical and genetic maps. (XLSX)<br>Supplementary File S10. List of putative genes within a 10 kbp-window on each side of QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match Sorghum bicolor genome v3.1.1. (XLSX)<br>Supplementary File S11. List of 150 differentially expressed genes from sweet sorghum associated with QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match Sorghum bicolor genome v3.1.1. (XLSX)<br>Supplementary File S12. List of 50 known sucrose-related associated with QTLs detected for Brandes × Wray sweet sorghum recombinant inbred lines. Coordinates were converted to match <i>Sorghum bicolor</i> genome v3.1.1. (XLSX)<br>Supplementary File S13. List of previously reported QTLs according to Sorghum QTL Atlas (https://aussorgm.org.au/sorghum-qtl-atlas/). (XLSX)
DOI: 10.15468/78fyp2
title: The skull of the Early Triassic archosauromorph reptile Prolacerta broomi and its phylogenetic significance

description: This dataset contains the digitized treatments in Plazi based on the original journal article Modesto, Sean P., Sues, Hans-Dieter (2004): The skull of the Early Triassic archosauromorph reptile Prolacerta broomi and its phylogenetic significance. Zoological Journal of the Linnean Society 140 (3): 335-351, DOI: 10.1111/j.1096-3642.2003.00102.x, URL: https://academic.oup.com/zoolinnean/article-lookup/doi/10.1111/j.1096-3642.2003.00102.x
DOI: 10.5281/zenodo.5347971
title: Infant Gray Matter Development

description: Source data associated with Infant gray matter development project
DOI: 10.6084/m9.figshare.14616821
title: A scoping review of longitudinal airway microbiota studies

description: The role of the microbiota in inflammatory airway diseases is unclear. Antimicrobial therapies have predominantly been guided by culture results. However, molecular sequencing has shown that the airway microbiota is much more complex and accurate modeling requires longitudinal analysis. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses scoping review was performed by searching Medline, Scopus, and Web of Science databases for all longitudinal airway microbiota studies that utilized molecular techniques. 38 studies with 1,993 participants were included in this review. Healthy microbial communities were more diverse, individualized and stable over time. Acute infections resulted in changes in the microbiota that were detected earlier and more sensitively by molecular sequencing than culture. Distinct microbiota profiles have been demonstrated in chronic obstructive pulmonary disease patients associated with exacerbation frequency and severity. Longitudinal studies provide essential data on the stability of the microbiota over time and valuable information about the dynamic interactions between host, disease and microbes. We believe that molecular sequencing will be increasingly incorporated into research and clinical practice in the future. These advances can lead to improved diagnosis, enhanced prescribing guidance and reduce unnecessary antibiotic usage.
DOI: 10.6084/m9.figshare.14616821.v1
title: A scoping review of longitudinal airway microbiota studies

description: The role of the microbiota in inflammatory airway diseases is unclear. Antimicrobial therapies have predominantly been guided by culture results. However, molecular sequencing has shown that the airway microbiota is much more complex and accurate modeling requires longitudinal analysis. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses scoping review was performed by searching Medline, Scopus, and Web of Science databases for all longitudinal airway microbiota studies that utilized molecular techniques. 38 studies with 1,993 participants were included in this review. Healthy microbial communities were more diverse, individualized and stable over time. Acute infections resulted in changes in the microbiota that were detected earlier and more sensitively by molecular sequencing than culture. Distinct microbiota profiles have been demonstrated in chronic obstructive pulmonary disease patients associated with exacerbation frequency and severity. Longitudinal studies provide essential data on the stability of the microbiota over time and valuable information about the dynamic interactions between host, disease and microbes. We believe that molecular sequencing will be increasingly incorporated into research and clinical practice in the future. These advances can lead to improved diagnosis, enhanced prescribing guidance and reduce unnecessary antibiotic usage.
DOI: 10.5517/ccdc.csd.cc280gj9
title: CCDC 2085820: Experimental Crystal Structure Determination

description: Related Article: Max Attwood, Hiroki Akutsu, Lee Martin, Toby J. Blundell, Pierre Le Maguere, Scott S. Turner|2021|Dalton Trans.|50|11843|doi:10.1039/D1DT01743H
DOI: 10.5517/ccdc.csd.cc280gkb
title: CCDC 2085821: Experimental Crystal Structure Determination

description: Related Article: Max Attwood, Hiroki Akutsu, Lee Martin, Toby J. Blundell, Pierre Le Maguere, Scott S. Turner|2021|Dalton Trans.|50|11843|doi:10.1039/D1DT01743H
DOI: 10.5517/ccdc.csd.cc28gc15
title: CCDC 2099166: Experimental Crystal Structure Determination

description: Related Article: Andie R. Delaney, Li-Juan Yu, Michelle L. Coote, Annie L. Colebatch|2021|Dalton Trans.|50|11909|doi:10.1039/D1DT01741A
DOI: 10.5517/ccdc.csd.cc280sz2
title: CCDC 2086144: Experimental Crystal Structure Determination

description: Related Article: Andie R. Delaney, Li-Juan Yu, Michelle L. Coote, Annie L. Colebatch|2021|Dalton Trans.|50|11909|doi:10.1039/D1DT01741A
DOI: 10.5517/ccdc.csd.cc280t04
title: CCDC 2086145: Experimental Crystal Structure Determination

description: Related Article: Andie R. Delaney, Li-Juan Yu, Michelle L. Coote, Annie L. Colebatch|2021|Dalton Trans.|50|11909|doi:10.1039/D1DT01741A
DOI: 10.5517/ccdc.csd.cc280sx0
title: CCDC 2086142: Experimental Crystal Structure Determination

description: Related Article: Andie R. Delaney, Li-Juan Yu, Michelle L. Coote, Annie L. Colebatch|2021|Dalton Trans.|50|11909|doi:10.1039/D1DT01741A
DOI: 10.48366/r141349
title: Nitrogen uptake rates in the northern and the southern Indian Ocean

description: This comparative analysis compares the Nitrogen uptake (i.e., NO3, NH4, urea or total Nitrogen) rates during different seasons in the northern and the southern Indian Ocean.
DOI: 10.26300/r8an-hw79
title: Analysis and reference data to generate figures for "Regional mixed layer depth as a climate diagnostic and emergent constraint"
DOI: 10.15468/q6fdy2
title: The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis

description: This dataset contains the digitized treatments in Plazi based on the original journal article Wirkner, Christian S., Richter, Stefan (2007): The circulatory system and its spatial relations to other major organ systems in Spelaeogriphacea and Mictacea (Malacostraca, Crustacea) - a three-dimensional analysis. Zoological Journal of the Linnean Society 149 (4): 629-642, DOI: 10.1111/j.1096-3642.2007.00274.x, URL: https://academic.oup.com/zoolinnean/article-lookup/doi/10.1111/j.1096-3642.2007.00274.x
DOI: 10.5281/zenodo.4632471
title: VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories

description: Single cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bio- conductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Here, we have included the data used in the package vignettes: <br> <br> 1) Pancreas endocrinogenesis data was obtained from Bergen et. al. Nature Biotechnology 2020 and Bastidas-Ponce et. al. Development 2019 via the scVelo package. - pancreas.rda contains spliced and unspliced count matrices, list of cluster IDs, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R - pancreasWithGap.rda includes the same data as in pancreas rda but with a subset of intermediate cells () removed to simulate data with missing intermediates. 2) MERFISH data was obtained from Xia et. al. PNAS 2019 - MERFISH.rda contains nuclear and cytoplasmic counts, colors used for plotting based on Louvain clustering, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R
DOI: 10.5281/zenodo.4632470
title: VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories

description: Single cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bio- conductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Here, we have included the data used in the package vignettes: <br> <br> 1) Pancreas endocrinogenesis data was obtained from Bergen et. al. Nature Biotechnology 2020 and Bastidas-Ponce et. al. Development 2019 via the scVelo package. - pancreas.rda contains spliced and unspliced count matrices, list of cluster IDs, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R - pancreasWithGap.rda includes the same data as in pancreas rda but with a subset of intermediate cells () removed to simulate data with missing intermediates. 2) MERFISH data was obtained from Xia et. al. PNAS 2019 - MERFISH.rda contains nuclear and cytoplasmic counts, colors used for plotting based on Louvain clustering, the first 50 principal components, cell-cell distances used in RNA velocity calculation, and the velocity object resulting from calculating velocity using velocyto.R
DOI: 10.48366/r141369
title: Biological nitrogen fixation assessment of the north and tropical Atlantic Ocean

description: This comparative analysis compares the biological nitrogen fixation rates during different sampling period in the north and tropical Atlantic Ocean.
DOI: 10.6073/pasta/20d32be4dadfa86af5392c1c23298c0b
title: Monsoon Rainfall Manipulation Experiment (MRME): Soil Temperature Data from the Sevilleta National Wildlife Refuge, NM

description: The Monsoon Rainfall Manipulation Experiment (MRME) is to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, which alters the pulses of soil moisture that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. These data are soil temperature data collected at two depths.
DOI: 10.48366/r141385
title: Biological nitrogen fixation assessment of the Atlantic Ocean

description: This comparative analysis compares the biological nitrogen fixation rates during different sampling period in the Atlantic Ocean.
DOI: 10.6084/m9.figshare.16550016
title: Lack of knowledge of the epigenetic-COVID-19 relationship: a case study_DATABASE

description: Database of the paper: <b><i>Lack of knowledge of the epigenetic-COVID-19 relationship: a case study</i></b><b><i></i></b>