cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12870412
title: Additional file 2 of The effects of anti-inflammatory agents as host-directed adjunct treatment of tuberculosis in humans: a systematic review and meta-analysis

description: Additional file 2: Supplementary Table 1. Quality assessment of included studies according to the Jadad scale. Supplementary Table 2. Risk of bias assessment of individual studies included. Supplementary Table 3. Frequency of outcome assessment and follow-up duration. Supplementary Table 4a. Outcome measurement at end point by allocation. Supplementary Table 4b. Outcome measurement at end point by allocation. Supplementary Table 4c. Outcome measurement at end point by allocation. Supplementary Figure 1. Funnel plot for aggregate patient data meta-analysis of sputum smear conversion rate conversion in vitamin D supplemented randomized controlled trials. Supplementary Figure 2. Funnel plot for aggregate patient data meta-analysis of sputum smear conversion rate in other anti-inflammatory HDT agents supplemented randomized controlled trials.
DOI: 10.6084/m9.figshare.8243213.v1
title: Organocatalytic Ring-Opening Polymerization by Fluorinated Alcohol: Fulfilling Cocatalyst-Free, Metal-Free, High-Rate and High-Selectivity

description: we describe a fluorinated alcohol based catalytic system for polypeptide synthesis via catalytic ring-opening polymerization (ROP) of <i>α</i>-amino acid <i>N</i>-carboxyanhydride (NCA), fulfilling cocatalyst-free, metal-free, high-rate and high-selectivity. The new finding not only represents a new catalysis mode of fluorinated alcohol but also provides a metal-free and fast approach to access well-defined polypeptides, mimics of natural analogs with extensive applications.
DOI: 10.6084/m9.figshare.8243168
title: Data and code from: Estimating the burden of dengue and the impact of release of wMel Wolbachia infected mosquitoes in Indonesia: a modelling study

description: The datasets and code from the paper [to add link].
DOI: 10.6084/m9.figshare.8243222.v2
title: Heterogeneous Stock Genotype Data

description: This is genotype data called from ddGBS data in the heterogeneous stock rats relevant to publication: Adapting genotyping-by-sequencing and variant calling for heterogeneous stock rats
DOI: 10.6084/m9.figshare.8243168.v1
title: Data and code from: Estimating the burden of dengue and the impact of release of wMel Wolbachia infected mosquitoes in Indonesia: a modelling study

description: The datasets and code from the paper [to add link].
DOI: 10.6084/m9.figshare.8243222.v1
title: Heterogeneous Stock Genotype Data

description: This is genotype data called from ddGBS data in the heterogeneous stock rats relevant to publication: Adapting genotyping-by-sequencing and variant calling for heterogeneous stock rats
DOI: 10.6084/m9.figshare.8243111
title: neighbour-joining_tree_micobes_of_interest

description: he evolutionary history was inferred using the Neighbor-Joining method [1]. The optimal tree with the sum of branch length = 2.24184989 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (10000 replicates) are shown next to the branches [2]. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Tamura 3-parameter method [3] and are in the units of the number of base substitutions per site. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). The analysis involved 37 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 155 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [4].<br><br>1. Saitou N. and Nei M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4:406-425.2. Felsenstein J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.3. Tamura K. (1992). Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.4. Kumar S., Stecher G., and Tamura K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets.Molecular Biology and Evolution 33:1870-1874.Disclaimer: Although utmost care has been taken to ensure the correctness of the caption, the caption text is provided "as is" without any warranty of any kind. Authors advise the user to carefully check the caption prior to its use for any purpose and report any errors or problems to the authors immediately (www.megasoftware.net). In no event shall the authors and their employers be liable for any damages, including but not limited to special, consequential, or other damages. Authors specifically disclaim all other warranties expressed or implied, including but not limited to the determination of suitability of this caption text for a specific purpose, use, or application.
DOI: 10.6084/m9.figshare.8243045
title: Scalable Bayesian Nonparametric Clustering and Classification

description: We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach make inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating datasets: a large set of electronic health records and a bank telemarketing dataset. We find interesting clusters and competitive classification performance relative to other widely used competing classifiers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8243111.v1
title: Neighbour-Joining tree of micobes of interest

description: he evolutionary history was inferred using the Neighbor-Joining method [1]. The optimal tree with the sum of branch length = 2.24184989 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (10000 replicates) are shown next to the branches [2]. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Tamura 3-parameter method [3] and are in the units of the number of base substitutions per site. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). The analysis involved 37 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 155 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [4].<br><br>1. Saitou N. and Nei M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4:406-425.2. Felsenstein J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.3. Tamura K. (1992). Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.4. Kumar S., Stecher G., and Tamura K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets.Molecular Biology and Evolution 33:1870-1874.Disclaimer: Although utmost care has been taken to ensure the correctness of the caption, the caption text is provided "as is" without any warranty of any kind. Authors advise the user to carefully check the caption prior to its use for any purpose and report any errors or problems to the authors immediately (www.megasoftware.net). In no event shall the authors and their employers be liable for any damages, including but not limited to special, consequential, or other damages. Authors specifically disclaim all other warranties expressed or implied, including but not limited to the determination of suitability of this caption text for a specific purpose, use, or application.
DOI: 10.6084/m9.figshare.8243111.v3
title: neighbour-joining_tree_micobes_of_interest

description: he evolutionary history was inferred using the Neighbor-Joining method [1]. The optimal tree with the sum of branch length = 2.24184989 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (10000 replicates) are shown next to the branches [2]. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Tamura 3-parameter method [3] and are in the units of the number of base substitutions per site. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). The analysis involved 37 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 155 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [4].<br><br>1. Saitou N. and Nei M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4:406-425.2. Felsenstein J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.3. Tamura K. (1992). Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.4. Kumar S., Stecher G., and Tamura K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets.Molecular Biology and Evolution 33:1870-1874.Disclaimer: Although utmost care has been taken to ensure the correctness of the caption, the caption text is provided "as is" without any warranty of any kind. Authors advise the user to carefully check the caption prior to its use for any purpose and report any errors or problems to the authors immediately (www.megasoftware.net). In no event shall the authors and their employers be liable for any damages, including but not limited to special, consequential, or other damages. Authors specifically disclaim all other warranties expressed or implied, including but not limited to the determination of suitability of this caption text for a specific purpose, use, or application.
DOI: 10.6084/m9.figshare.8243045.v2
title: Scalable Bayesian Nonparametric Clustering and Classification

description: We develop a scalable multistep Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach make inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating datasets: a large set of electronic health records and a bank telemarketing dataset. We find interesting clusters and competitive classification performance relative to other widely used competing classifiers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8243045.v1
title: Scalable Bayesian Nonparametric Clustering and Classification

description: We develop a scalable multi-step Monte Carlo algorithm for inference under a large class of nonparametric Bayesian models for clustering and classification. Each step is “embarrassingly parallel” and can be implemented using the same Markov chain Monte Carlo sampler. The simplicity and generality of our approach makes inference for a wide range of Bayesian nonparametric mixture models applicable to large datasets. Specifically, we apply the approach to inference under a product partition model with regression on covariates. We show results for inference with two motivating data sets: a large set of electronic health records (EHR) and a bank telemarketing dataset. We find interesting clusters and competitive classification performance relative to other widely used competing classifiers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8243111.v2
title: neighbour-joining _tree_micobes_of_interest

description: he evolutionary history was inferred using the Neighbor-Joining method [1]. The optimal tree with the sum of branch length = 2.24184989 is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (10000 replicates) are shown next to the branches [2]. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The evolutionary distances were computed using the Tamura 3-parameter method [3] and are in the units of the number of base substitutions per site. The rate variation among sites was modeled with a gamma distribution (shape parameter = 1). The analysis involved 37 nucleotide sequences. All positions containing gaps and missing data were eliminated. There were a total of 155 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 [4].<br><br>1. Saitou N. and Nei M. (1987). The neighbor-joining method: A new method for reconstructing phylogenetic trees. Molecular Biology and Evolution 4:406-425.2. Felsenstein J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.3. Tamura K. (1992). Estimation of the number of nucleotide substitutions when there are strong transition-transversion and G + C-content biases. Molecular Biology and Evolution 9:678-687.4. Kumar S., Stecher G., and Tamura K. (2016). MEGA7: Molecular Evolutionary Genetics Analysis version 7.0 for bigger datasets.Molecular Biology and Evolution 33:1870-1874.Disclaimer: Although utmost care has been taken to ensure the correctness of the caption, the caption text is provided "as is" without any warranty of any kind. Authors advise the user to carefully check the caption prior to its use for any purpose and report any errors or problems to the authors immediately (www.megasoftware.net). In no event shall the authors and their employers be liable for any damages, including but not limited to special, consequential, or other damages. Authors specifically disclaim all other warranties expressed or implied, including but not limited to the determination of suitability of this caption text for a specific purpose, use, or application.
DOI: 10.6084/m9.figshare.8242898
title: Petroleum_System_Chart

description: Simple petroleum systems chart in excel to record information such as source, reservoir, seal, trap and generation. Typically used in conjunction with data collated in a Geological Events Chart (https://doi.org/10.6084/m9.figshare.8242802.v1).<br><br>
DOI: 10.6084/m9.figshare.8242883
title: greengenes_taxonomy_file

description: Taxonomy file for NGS analysis
DOI: 10.6084/m9.figshare.8242889
title: Data for "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"

description: Data for the paper entitled "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"
DOI: 10.6084/m9.figshare.8242889.v2
title: Data for "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"

description: Data for the paper entitled "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"
DOI: 10.6084/m9.figshare.8242883.v1
title: greengenes_taxonomy_file

description: Taxonomy file for NGS analysis
DOI: 10.6084/m9.figshare.8242868
title: mapping_file

description: Metadata for NGS analysis
DOI: 10.6084/m9.figshare.8242889.v1
title: Data for "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"

description: Data for the paper entitled "Bilateral and unilateral load-velocity profiling in a machine-based, single-joint, lower body exercise: A proof of concept"
DOI: 10.6084/m9.figshare.8242868.v1
title: mapping_file

description: Metadata for NGS analysis
DOI: 10.6084/m9.figshare.8242904
title: Supplemental data for Montuelle et al. Unilateral lingual nerve transection alters jaw-tongue coordination during mastication in pigs

description: Files contain experiment metadata and results of sequence level cross-correlation analyses for jaw-tongue coordination pre- and post-unilateral lingual nerve transection in pigs
DOI: 10.6084/m9.figshare.8242859
title: 2019 ISC High Performance Conference Tutorial: Better Scientific Software

description: The computational science and engineering (CSE) community is in the midst of an extremely challenging period created by the confluence of disruptive changes in computing architectures, demand for greater scientific reproducibility, and new opportunities for greatly improved simulation capabilities, especially through coupling physics and scales. Computer architecture changes require new software design and implementation strategies, including significant refactoring of existing code. Reproducibility demands require more rigor across the entire software endeavor. Code coupling requires aggregate team interactions including integration of software processes and practices. These challenges demand large investments in scientific software development and improved practices. Focusing on improved developer productivity and software sustainability is both urgent and essential.<br>This half day tutorial distills multi-project and multi-years’ experience from members of the IDEAS project, and creators of the BSSw.io community website. It provides information and hands-on experience with software practices, processes, and tools explicitly tailored for CSE. Goals are improving the productivity of those who develop CSE software and increasing the sustainability of software artifacts. We discuss practices that are relevant for projects of all sizes, with emphasis on small teams, and on aggregate teams composed of small teams. Topics include reasons and motivation for caring about software productivity in science teams, effective models, tools, and processes for small teams (including agile workflow management), reproducibility, and scientific software testing and verification (including automated testing and continuous integration), and refactoring.<br>Outline <br>* Why effective software practices are essential for CSE projects [40 min] - Terminology - Understanding what you want from your CSE software and how to achieve it * Effective models, tools, processes and practices for CSE software teams [40 min] - Small team models, challenges - Aggregate team models, challenges - Agile workflow management - Hands on: Issue tracking via Kanban in GitHub * Reproducibility [40 min] - Increasing demand for reproducibility - Role of better software practices to support reproducibility - Preparing for next-generation publication requirements * Software testing and evolution [45 min] - Introduction and motivation - How to develop tests and test-suites - Planning and verification for refactoring * Software maintenance [45 min] - Workflow management with git - Using Travis CI including review of Hello World CI example.<br>We consider this tutorial to have a mix of content, with the majority being at the beginning and intermediate levels.
DOI: 10.6084/m9.figshare.8242904.v2
title: Supplemental data for Montuelle et al. Unilateral lingual nerve transection alters jaw-tongue coordination during mastication in pigs

description: File contains experiment metadata and results of sequence level cross-correlation analyses for jaw-tongue coordination pre- and post-lingual unilateral nerve transection in pigs
DOI: 10.6084/m9.figshare.8242859.v1
title: 2019 ISC High Performance Conference Tutorial: Better Scientific Software

description: The computational science and engineering (CSE) community is in the midst of an extremely challenging period created by the confluence of disruptive changes in computing architectures, demand for greater scientific reproducibility, and new opportunities for greatly improved simulation capabilities, especially through coupling physics and scales. Computer architecture changes require new software design and implementation strategies, including significant refactoring of existing code. Reproducibility demands require more rigor across the entire software endeavor. Code coupling requires aggregate team interactions including integration of software processes and practices. These challenges demand large investments in scientific software development and improved practices. Focusing on improved developer productivity and software sustainability is both urgent and essential.<br>This half day tutorial distills multi-project and multi-years’ experience from members of the IDEAS project, and creators of the BSSw.io community website. It provides information and hands-on experience with software practices, processes, and tools explicitly tailored for CSE. Goals are improving the productivity of those who develop CSE software and increasing the sustainability of software artifacts. We discuss practices that are relevant for projects of all sizes, with emphasis on small teams, and on aggregate teams composed of small teams. Topics include reasons and motivation for caring about software productivity in science teams, effective models, tools, and processes for small teams (including agile workflow management), reproducibility, and scientific software testing and verification (including automated testing and continuous integration), and refactoring.<br>Outline <br>* Why effective software practices are essential for CSE projects [40 min] - Terminology - Understanding what you want from your CSE software and how to achieve it * Effective models, tools, processes and practices for CSE software teams [40 min] - Small team models, challenges - Aggregate team models, challenges - Agile workflow management - Hands on: Issue tracking via Kanban in GitHub * Reproducibility [40 min] - Increasing demand for reproducibility - Role of better software practices to support reproducibility - Preparing for next-generation publication requirements * Software testing and evolution [45 min] - Introduction and motivation - How to develop tests and test-suites - Planning and verification for refactoring * Software maintenance [45 min] - Workflow management with git - Using Travis CI including review of Hello World CI example.<br>We consider this tutorial to have a mix of content, with the majority being at the beginning and intermediate levels.
DOI: 10.6084/m9.figshare.8242802
title: Geological_Event_Chart

description: Geological events chart in excel, includes columns to input information on the plate tectonic setting, regional tectonic setting, depositional environment and lithostratigraphy. This list is not exhaustive and should be used to build on, adding your own (e.g. local datasets, petroleum systems, data by basin or quad).<br>Age (Ma) based on the International Chronostratigraphic Chart v2018/08 by the International Commission on Stratigraphy. <br>Use this spreadsheet as a basis for collating and cataloging information when starting a new project or when working in a new area. Allows tectonostratigraphic data to be laid out chronologically along the geological timescale.
DOI: 10.6084/m9.figshare.8242904.v4
title: Supplemental data for Montuelle et al. Unilateral lingual nerve transection alters jaw-tongue coordination during mastication in pigs

description: Files contain experiment metadata and results of sequence level cross-correlation analyses for jaw-tongue coordination pre- and post-unilateral lingual nerve transection in pigs