cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1606304.v1
title: Informative Sensor and Feature Selection via Hierarchical Nonnegative Garrote

description: Placing sensors in every station of a process or every element of a system to monistor its state or performance is usually too expensive or physically impossible. Therefore, a systematic method is needed to select important sensing variables. The method should not only be capable of identifying important sensors/signals among multistream signals from a distributed sensing system, but should also be able to extract a small set of interpretable features from the high-dimensional vector of a selected signal. For this purpose, we develop a new hierarchical regularization approach called hierarchical nonnegative garrote (NNG). At the first level of hierarchy, a group NNG is used to select important signals, and at the second level, the individual features within each signal are selected using a modified version of NNG that possesses good properties for the estimated coefficients. Performance of the proposed method is evaluated and compared with other existing methods through Monte Carlo simulation. A case study is conducted to demonstrate the proposed methodology that can be applied to develop a predictive model for the assessment of vehicle design comfort based on the tested drivers’ motion trajectory signals. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1606311.v1
title: How to think about Machine Learning

description: Lecture by Cheng Soon Ong about how to think about Machine Learning presented at the Machine Learning for Biology Day at ANU.
DOI: 10.6084/m9.figshare.1606298
title: A Semiparametric Software Reliability Model for Analysis of a Bug-Database With Multiple Defect Types

description: Software bug-databases provide an important source of data for assessing the reliability of a software product after its release. Statistical analysis of these databases can be challenging when software usage is unknown, that is, there is no information about the usage, either in the form of a parametric model, or in the form of actual measurements. Reliability metrics, when defined on a calendar time scale, would depend on this unknown and time-dependent usage of the software and hence cannot be estimated. This article proposes a semiparametric analysis that makes use of defect classifications into multiple types to enable estimation of a model without making strict assumptions about the underlying usage of the software. New reliability metrics whose computation does not depend on the unknown usage of the software have been proposed and methods for estimating them have been developed. The proposed method has been illustrated using data retrieved from the bug-database of a popular scripting language, named Python. This illustration compares reliability of two versions of the software without making assumptions about their unknown usage. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1606300.v1
title: General Blending Models for Data From Mixture Experiments

description: We propose a new class of models providing a powerful unification and extension of existing statistical methodology for analysis of data obtained in mixture experiments. These models, which integrate models proposed by Scheffé and Becker, extend considerably the range of mixture component effects that may be described. They become complex when the studied phenomenon requires it, but remain simple whenever possible. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1606299
title: Matrix Discriminant Analysis With Application to Colorimetric Sensor Array Data

description: With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) that is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors that rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material.
DOI: 10.6084/m9.figshare.1606299.v1
title: Matrix Discriminant Analysis With Application to Colorimetric Sensor Array Data

description: With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) that is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors that rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material.
DOI: 10.6084/m9.figshare.1606298.v1
title: A Semiparametric Software Reliability Model for Analysis of a Bug-Database With Multiple Defect Types

description: Software bug-databases provide an important source of data for assessing the reliability of a software product after its release. Statistical analysis of these databases can be challenging when software usage is unknown, that is, there is no information about the usage, either in the form of a parametric model, or in the form of actual measurements. Reliability metrics, when defined on a calendar time scale, would depend on this unknown and time-dependent usage of the software and hence cannot be estimated. This article proposes a semiparametric analysis that makes use of defect classifications into multiple types to enable estimation of a model without making strict assumptions about the underlying usage of the software. New reliability metrics whose computation does not depend on the unknown usage of the software have been proposed and methods for estimating them have been developed. The proposed method has been illustrated using data retrieved from the bug-database of a popular scripting language, named Python. This illustration compares reliability of two versions of the software without making assumptions about their unknown usage. This article has supplementary material online.
DOI: 10.6084/m9.figshare.1606266
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606294
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606265
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606263
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606265.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606264
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606294.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606262
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606263.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606266.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurK biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606259
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606260
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606261
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606264.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606258
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606260.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606262.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurC biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606257
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 with 0.2mM adenine at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606256
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606259.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606261.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the ΔpurH biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606253
title: Phylogenetic and Functional Substrate Specificity for Endolithic Microbial Communities from the Atacama Desert

description: Under extreme water deficit, large thermal fluctuation, and high solar radiation fluxes, endolithic (inhabiting rock) microbial ecosystems are considered environmental refuges for life in cold and hot deserts around the world. Metagenomics analysis of two rock substrates, calcite (calcium carbonate) and ignimbrite (volcanic rock), revealed that the communities were dominated by Cyanobacteria, Actinobacteria, and Chloroflexi, but that the relative distribution of these phyla was significantly different between the two substrates.
DOI: 10.6084/m9.figshare.1606255
title: Gaussian Job Archive for C6H20O8

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1606258.v2
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 at 9hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606255.v1
title: Gaussian Job Archive for C6H20O8

description: Archive of work produced using the resources of the Imperial College High Performance Computing Service.
DOI: 10.6084/m9.figshare.1606258.v1
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 at 9hrs

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606253.v1
title: Phylogenetic and Functional Substrate Specificity for Endolithic Microbial Communities from the Atacama Desert

description: Under extreme water deficit, large thermal fluctuation, and high solar radiation fluxes, endolithic (inhabiting rock) microbial ecosystems are considered environmental refuges for life in cold and hot deserts around the world. Metagenomics analysis of two rock substrates, calcite (calcium carbonate) and ignimbrite (volcanic rock), revealed that the communities were dominated by Cyanobacteria, Actinobacteria, and Chloroflexi, but that the relative distribution of these phyla was significantly different between the two substrates.
DOI: 10.6084/m9.figshare.1606256.v2
title: The raw data and the results of the particle metric analysis performed for the SEM images of the WT biofilm cells in K10T-1 at 6hrs.

description: The “SEM images” folder contains the original SEM images obtained for the analyses, and the selected particles are shown in several colors in the each image in the “Particle images” folder. The “particle.csv” is the excel file in which information on each particle is described. The “report.doc” file is the summary of the analysis that contains statistical data on the analysis.
DOI: 10.6084/m9.figshare.1606233
title: Cost-effectiveness of combined sexual and injection risk reduction interventions among Female Sex Workers who inject drugs in two very distinct Mexican border cities

description: Data used for cost effectiveness analysis for the Mujer Mas Segura brief behavioral intervention.