cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9253424
title: March_0125m_annot_count_table.txtDiel oscillation of microbial gene transcription at different depths in oligotrophic ocean waters

description: These are count tables of annotated transcript abundances for planktonic microbial populations, taken every 4 hours at different depths in the North Pacific Subtroptical Gyre. They are part of the dataset included in a manuscript in review at Frontiers in Microbiology entitled: Diel oscillation of microbial gene transcription declines with increasing depth in oligotrophic ocean waters, by Alice Vislova, Oscar Sosa, John M. Eppley, Anna E. Romano, and Edward F. DeLong. Deposited August 5, 2019.These datasets are count tables of annotated transcript abundances for planktonic microbial populations, that were sampled every 4 hours at different depths in the North Pacific Subtroptical Gyre. They are part of the dataset included in a manuscript now in review at Frontiers in Microbiology entitled: Diel oscillation of microbial gene transcription declines with increasing depth in oligotrophic ocean waters, by Alice Vislova, Oscar Sosa, John M. Eppley, Anna E. Romano, and Edward F. DeLong. Deposited August 5, 2019.
DOI: 10.6084/m9.figshare.9253412
title: LGWO_SVR model for dam deformation prediction

description: The levy flight-based grey wolf algorithm-based support vector machine
DOI: 10.6084/m9.figshare.9253412.v1
title: LGWO_SVR model for dam deformation prediction

description: The levy flight-based grey wolf algorithm-based support vector machine
DOI: 10.6084/m9.figshare.9253424.v2
title: March_0125m_annot_count_table.txtDiel oscillation of microbial gene transcription at different depths in oligotrophic ocean waters

description: These are count tables of annotated transcript abundances for planktonic microbial populations, taken every 4 hours at different depths in the North Pacific Subtroptical Gyre. They are part of the dataset included in a manuscript in review at Frontiers in Microbiology entitled: Diel oscillation of microbial gene transcription declines with increasing depth in oligotrophic ocean waters, by Alice Vislova, Oscar Sosa, John M. Eppley, Anna E. Romano, and Edward F. DeLong. Deposited August 5, 2019.These datasets are count tables of annotated transcript abundances for planktonic microbial populations, that were sampled every 4 hours at different depths in the North Pacific Subtroptical Gyre. They are part of the dataset included in a manuscript now in review at Frontiers in Microbiology entitled: Diel oscillation of microbial gene transcription declines with increasing depth in oligotrophic ocean waters, by Alice Vislova, Oscar Sosa, John M. Eppley, Anna E. Romano, and Edward F. DeLong. Deposited August 5, 2019.
DOI: 10.6084/m9.figshare.9253382
title: Raw data of Morphometric variation (in mm) of Megophrys spp.

description: Raw data on the morphometric variations of <i>Megophrys</i> spp. for character abbreviations see (Munir, M., Hamidy, A., Farajallah, A. & Smith, E.N. (2018) A New <i>Megophrys</i> Kuhl and van Hasselt (Amphibia: Megophryidae) from southwestern Sumatra, Indonesia.<i> Zootaxa, </i>4442 (3), 389-412.)
DOI: 10.6084/m9.figshare.9253391
title: Gene expression levels od Troponin T Amino Acid Mutation (ΔK 210) Knock-in Mice as a Neonatal Dilated Cardiomyopathy Model at 0day and 1week after birth

description: Cardiac phenotypes of homozygous ΔK210 knock-in (TNNT2ΔK210/ΔK210) mice including comprehensive gene expression profiles were investigated to establish an animal model of neonatal DCM and to identify early progression factors of pediatric DCM.
DOI: 10.6084/m9.figshare.9253424.v1
title: March_0125m_annot_count_table.txt

description: These are count tables of annotated transcript abundances for planktonic microbial populations, taken every 4 hours at different depths in the North Pacific Subtroptical Gyre. They are part of the dataset included in a manuscript in review at Frontiers in Microbiology entitled: Diel oscillation of microbial gene transcription declines with increasing depth in oligotrophic ocean waters, by Alice Vislova, Oscar Sosa, John M. Eppley, Anna E. Romano, and Edward F. DeLong. Deposited August 5, 2019.
DOI: 10.6084/m9.figshare.9253379
title: Data for paper "Superadditive and subadditive dynamics are not inherent to the types of interacting threat"

description: Please consider this data in conjunction with the paper. The methods on how the data was derived can be found in the paper published by PLOS One
DOI: 10.6084/m9.figshare.9253382.v1
title: Raw data of Morphometric variation (in mm) of Megophrys spp.

description: Raw data on the morphometric variations of <i>Megophrys</i> spp. for character abbreviations see (Munir, M., Hamidy, A., Farajallah, A. & Smith, E.N. (2018) A New <i>Megophrys</i> Kuhl and van Hasselt (Amphibia: Megophryidae) from southwestern Sumatra, Indonesia.<i> Zootaxa, </i>4442 (3), 389-412.)
DOI: 10.6084/m9.figshare.9253391.v1
title: Gene expression levels od Troponin T Amino Acid Mutation (ΔK 210) Knock-in Mice as a Neonatal Dilated Cardiomyopathy Model at 0day and 1week after birth

description: Cardiac phenotypes of homozygous ΔK210 knock-in (TNNT2ΔK210/ΔK210) mice including comprehensive gene expression profiles were investigated to establish an animal model of neonatal DCM and to identify early progression factors of pediatric DCM.
DOI: 10.6084/m9.figshare.9253379.v1
title: Data for paper "Superadditive and subadditive dynamics are not inherent to the types of interacting threat"

description: Please consider this data in conjunction with the paper. The methods on how the data was derived can be found in the paper published by PLOS One
DOI: 10.6084/m9.figshare.9253217
title: Quantitation of redistribution of row 1 and 2 proteins in transduction mutants

description: Krey et al. Fig. 5 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from transduction mutants. <br>Krey et al. Fig. 8 data.zip contains spreadsheets with fluorescence quantitation for row 2 proteins in tips of mice from transduction mutants.<br>Krey et al. Fig. S4 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from aider range of genotypes of transduction mutants.<br>We estimated the total immunofluorescence signal in the top 1-2 µm of each tip. Airyscan z-stacks were imported into Fiji Software, which was used for all analysis steps. For analysis of P7.5 mutant mice, average Z-projections of Airyscan stacks were made that included row 1 and row 2 tips in the same projection. For analysis of P21.5 mutant mice, due to increased height variability, Regions of Interest (ROIs) were selected at row 1 or row 2 tips within individual x-y slices from the z-stacks. Images were kept as multi-channel stacks and phalloidin was used to guide selection at the tips of each row. Regions of interest (ROIs) used circles that encompassed most of each tip. Area and average intensity measurements were made from ten or more row 1 tips and ten or more row 2 tips, and blank measurements were taken from volumes outside the stereocilia and above the epithelium. The total signal (area times average intensity) in each stereocilia tip volume was calculated after subtracting the background. For each hair bundle, the average signal in rows 1 and 2 was calculated, which was used to normalize the individual row 1 and row 2 stereocilia tip signals within each bundle. The average signal row 1 and 2 signal for each bundle was also used for comparison of expression levels at different developmental times or between control and mutant mice. For comparisons between transduction-mutant genotypes, the average signal for control bundles on a given experimental day was used to normalize all genotypes, allowing comparison of controls and mutants with identical acquisition parameters. .
DOI: 10.5522/04/9253187
title: When drug treatments bias genetic studies: data files

description: Numerical data underlying the figures of PLOS ONE paper: "When drug treatments bias genetic studies: mediation and interaction". <br>The files "Data_Figure3-4" pertain to simulation results described in the paper and appendix. <br>The file "Data_Figure5" contains aggregated level data on the proportion and type of diabetes medication prescribed over a 3 year period. Data were obtained from a Dutch diabetes cohort 'GAINTT': https://www.giantt.nl/nl/<br>The file "Data_Figure6" contains mean difference and p-values of 100+ genetic variants and their association with longitudinal (over a 3 year period) HbA1c levels. Results are stratified for treatment modelling strategy. <br><br>These data have been shared inline with the journal's open access policy.
DOI: 10.5522/04/9253187.v1
title: When drug treatments bias genetic studies: data files

description: Numerical data underlying the figures of PLOS ONE paper: "When drug treatments bias genetic studies: mediation and interaction". <br>The files "Data_Figure3-4" pertain to simulation results described in the paper and appendix. <br>The file "Data_Figure5" contains aggregated level data on the proportion and type of diabetes medication prescribed over a 3 year period. Data were obtained from a Dutch diabetes cohort 'GAINTT': https://www.giantt.nl/nl/<br>The file "Data_Figure6" contains mean difference and p-values of 100+ genetic variants and their association with longitudinal (over a 3 year period) HbA1c levels. Results are stratified for treatment modelling strategy. <br><br>These data have been shared inline with the journal's open access policy.
DOI: 10.6084/m9.figshare.9253130
title: formation_energy.extxyz

description: Dataset containing atomic geometries and formation energies for inorganic crystals extracted from OQMD 1.1.
DOI: 10.6084/m9.figshare.9253217.v1
title: Quantitation of redistribution of row 1 and 2 proteins in transduction mutants

description: Krey et al. Fig. 5 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from transduction mutants. <br>Krey et al. Fig. 8 data.zip contains spreadsheets with fluorescence quantitation for row 2 proteins in tips of mice from transduction mutants.<br>Krey et al. Fig. S4 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from aider range of genotypes of transduction mutants.<br>We estimated the total immunofluorescence signal in the top 1-2 µm of each tip. Airyscan z-stacks were imported into Fiji Software, which was used for all analysis steps. For analysis of P7.5 mutant mice, average Z-projections of Airyscan stacks were made that included row 1 and row 2 tips in the same projection. For analysis of P21.5 mutant mice, due to increased height variability, Regions of Interest (ROIs) were selected at row 1 or row 2 tips within individual x-y slices from the z-stacks. Images were kept as multi-channel stacks and phalloidin was used to guide selection at the tips of each row. Regions of interest (ROIs) used circles that encompassed most of each tip. Area and average intensity measurements were made from ten or more row 1 tips and ten or more row 2 tips, and blank measurements were taken from volumes outside the stereocilia and above the epithelium. The total signal (area times average intensity) in each stereocilia tip volume was calculated after subtracting the background. For each hair bundle, the average signal in rows 1 and 2 was calculated, which was used to normalize the individual row 1 and row 2 stereocilia tip signals within each bundle. The average signal row 1 and 2 signal for each bundle was also used for comparison of expression levels at different developmental times or between control and mutant mice. For comparisons between transduction-mutant genotypes, the average signal for control bundles on a given experimental day was used to normalize all genotypes, allowing comparison of controls and mutants with identical acquisition parameters. .
DOI: 10.6084/m9.figshare.9253178
title: Dataset for: Avoiding pitfalls when combining multiple imputation and propensity scores

description: Overcoming bias due to confounding and missing data is challenging when analysing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data.
DOI: 10.6084/m9.figshare.9253124
title: ionic_charge.extxyz

description: Dataset containing atomic geometries and ionic charges for the GDB-9 dataset.
DOI: 10.6084/m9.figshare.9253217.v2
title: Quantitation of redistribution of row 1 and 2 proteins in transduction mutants

description: Krey et al. Fig. 5 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from transduction mutants. <br>Krey et al. Fig. 8 data.zip contains spreadsheets with fluorescence quantitation for row 2 proteins in tips of mice from transduction mutants.<br>Krey et al. Fig. S4 data.zip contains spreadsheets with fluorescence quantitation for row 1 proteins in tips of mice from aider range of genotypes of transduction mutants.<br>We estimated the total immunofluorescence signal in the top 1-2 µm of each tip. Airyscan z-stacks were imported into Fiji Software, which was used for all analysis steps. For analysis of P7.5 mutant mice, average Z-projections of Airyscan stacks were made that included row 1 and row 2 tips in the same projection. For analysis of P21.5 mutant mice, due to increased height variability, Regions of Interest (ROIs) were selected at row 1 or row 2 tips within individual x-y slices from the z-stacks. Images were kept as multi-channel stacks and phalloidin was used to guide selection at the tips of each row. Regions of interest (ROIs) used circles that encompassed most of each tip. Area and average intensity measurements were made from ten or more row 1 tips and ten or more row 2 tips, and blank measurements were taken from volumes outside the stereocilia and above the epithelium. The total signal (area times average intensity) in each stereocilia tip volume was calculated after subtracting the background. For each hair bundle, the average signal in rows 1 and 2 was calculated, which was used to normalize the individual row 1 and row 2 stereocilia tip signals within each bundle. The average signal row 1 and 2 signal for each bundle was also used for comparison of expression levels at different developmental times or between control and mutant mice. For comparisons between transduction-mutant genotypes, the average signal for control bundles on a given experimental day was used to normalize all genotypes, allowing comparison of controls and mutants with identical acquisition parameters. .
DOI: 10.6084/m9.figshare.9252995
title: Supporting_Information.zip

description: <br>Supporting Information Summary for:<br>Single-molecule correlated chemical probing reveals large-scale structural communication in the ribosome and the mechanism of the antibiotic spectinomycin in living cells<br>Arnab Sengupta, Greggory M. Rice, and Kevin M. WeeksDepartment of ChemistryUniversity of North Carolina<br><br>This supporting information contains data and analysis files used in the 16S ribosome RING-MaP paper.<br><br>The [S1_File] archive contains the following:<br>-- [Analysis_pipeline]: Software programs used to calculate per-nucleotide reactivity rates, and correlation analysis. Files ending in .py can be executed in Python. This folder also contains a README.txt file containing specific details on using the software. Additional .txt files contain example data that can be used to test the programs. The folder named excluded nucleotides contains an indexed list of nucleotides excluded from analysis due to high background mutation. Most of these high background nucleotides are from SNPs in the multiple copies of the ribosomal gene present in E. coli.<br>-- [ArcPlot_CorrelationData_FS2]: RING correlation data and arc-plot diagrams for all treatment conditions and replicates. Treatment conditions are indicated by labels ex (extracted), IC (in-cell), Rif (rifampicin), Spc (spectinomycin), RifSpc (rifampicin and spectinomycin). Replicate information is indicated by numbers 1 and 2. Merged and average replicate data are also included as .txt and .pdf files. ArcPlot diagrams used in Fig. S2 were prepared using these data.<br>-- [Gephi_Data_F2BC_FS3]: Contains network analysis input files. Gephi output files use RING correlation data for network visualization. Files ending in .gephi can be opened in the Gephi software to visualize network diagrams. IC analysis files have information degree analysis and modularity class information under the different treatment conditions. The .svg files contains plots of correlation data superimposed on 16S RNA secondary structure maps. Files with the label "r" contain data for rifampicin treatment, "rs" contain data for rifampicin and spectinomycin treatment. Numerical labels (1-9) represent modularity class. Additional files contain information of nucleotide coloring by network community on the 16S secondary structure. <br>-- [PyMol_Sessions]: Pymol sessions files (.pse) used to prepare Figs. 2C, 3D, 3E, 4A, 4B, 4C, 4D, and 5. Sessions contain defined objects for distinct states and features. Scripts used to create pymol sessions, starting from the pdb file, are also included.<br><br>The file [S1_Data] contains per-nucleotide DMS reactivity data for conditions including extracted, in-cell, +Rif, and +RifSpc treatment.<br><br>The file [S2_Data] contains the underlying data used to prepare figures 2A, 3, S2, and S3.<br>
DOI: 10.6084/m9.figshare.9252992
title: Concatenated_alignment_SAR_only

description: Final concatenated dataset contains only Stramenopiles, Alveolates, and Rhizaria
DOI: 10.6084/m9.figshare.9252995.v1
title: Supporting_Information.zip

description: <br>Supporting Information Summary for:<br>Single-molecule correlated chemical probing reveals large-scale structural communication in the ribosome and the mechanism of the antibiotic spectinomycin in living cells<br>Arnab Sengupta, Greggory M. Rice, and Kevin M. WeeksDepartment of ChemistryUniversity of North Carolina<br><br>This supporting information contains data and analysis files used in the 16S ribosome RING-MaP paper.<br><br>The [S1_File] archive contains the following:<br>-- [Analysis_pipeline]: Software programs used to calculate per-nucleotide reactivity rates, and correlation analysis. Files ending in .py can be executed in Python. This folder also contains a README.txt file containing specific details on using the software. Additional .txt files contain example data that can be used to test the programs. The folder named excluded nucleotides contains an indexed list of nucleotides excluded from analysis due to high background mutation. Most of these high background nucleotides are from SNPs in the multiple copies of the ribosomal gene present in E. coli.<br>-- [ArcPlot_CorrelationData_FS2]: RING correlation data and arc-plot diagrams for all treatment conditions and replicates. Treatment conditions are indicated by labels ex (extracted), IC (in-cell), Rif (rifampicin), Spc (spectinomycin), RifSpc (rifampicin and spectinomycin). Replicate information is indicated by numbers 1 and 2. Merged and average replicate data are also included as .txt and .pdf files. ArcPlot diagrams used in Fig. S2 were prepared using these data.<br>-- [Gephi_Data_F2BC_FS3]: Contains network analysis input files. Gephi output files use RING correlation data for network visualization. Files ending in .gephi can be opened in the Gephi software to visualize network diagrams. IC analysis files have information degree analysis and modularity class information under the different treatment conditions. The .svg files contains plots of correlation data superimposed on 16S RNA secondary structure maps. Files with the label "r" contain data for rifampicin treatment, "rs" contain data for rifampicin and spectinomycin treatment. Numerical labels (1-9) represent modularity class. Additional files contain information of nucleotide coloring by network community on the 16S secondary structure. <br>-- [PyMol_Sessions]: Pymol sessions files (.pse) used to prepare Figs. 2C, 3D, 3E, 4A, 4B, 4C, 4D, and 5. Sessions contain defined objects for distinct states and features. Scripts used to create pymol sessions, starting from the pdb file, are also included.<br><br>The file [S1_Data] contains per-nucleotide DMS reactivity data for conditions including extracted, in-cell, +Rif, and +RifSpc treatment.<br><br>The file [S2_Data] contains the underlying data used to prepare figures 2A, 3, S2, and S3.<br>
DOI: 10.6084/m9.figshare.9252992.v2
title: Concatenated_alignment_SAR_only

description: Final concatenated dataset contains only Stramenopiles, Alveolates, and Rhizaria
DOI: 10.6084/m9.figshare.9252953
title: Québec Quantité d’unités d’émission versées en allocation gratuite

description: Periodic reports from Quebec's Ministère de l’Environnement et de la Lutte contre les Changements Climatiques (MELCC) stating quantities of allowances distributed without charge to industrial emitters. <br><br>For each emission year, subsequent reports state the total quantity of allowances distributed. The initial allocations, in January of each year, are 75% of the estimated total allocation for that same year. The major revision to the allocations occurs in September of the following year, in which the remaining 25% of the (revised) estimated total allocation is distributed. There have been further revisions to each allocation, some positive and some negative (meaning that emitters had to return allowances to regulators).<br>
DOI: 10.6084/m9.figshare.9252965
title: 120_single_gene_trees

description: Single gene trees of 120 conserved genes
DOI: 10.6084/m9.figshare.9252968
title: 120_gene-alignments

description: Single gene alignment in fasta format
DOI: 10.6084/m9.figshare.9252965.v1
title: 120_single_gene_trees

description: Single gene trees of 120 conserved genes