cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7010483
title: Dataset for: Power analysis for multivariable Cox regression models

description: In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because in many typical power analysis settings assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect. This approach is explored analytically and by simulations in the present paper. We consider several alternative variance expressions, and compare their performance to that of the traditional null variance expression. Theoretical analysis and simulations demonstrate that while the null variance expression performs well in many non-null settings, it can also be very inaccurate, substantially underestimating or overestimating the true variance in a wide range of realistic scenarios, particularly those where the numbers of treated and control subjects are very different and the true hazard ratio is not close to one. The alternative variance expressions have much better theoretical properties, confirmed in simulations. The most accurate of these expressions has a relatively simple form - it is the sum of inverse expected event counts under treatment and under control scaled up by a variance inflation factor.
DOI: 10.6084/m9.figshare.7010495.v3
title: Supplemental figures and files for "Assessment of genetic differentiation and linkage disequilibrium in Solanum pimpinellifolium using genome-wide high-density SNP markers."

description: <b>Figure S1</b><b>. The cross-validation error of K value in ADMIXTURE. </b>This figure shows the cross-validation of ADMIXTURE for the 98 <i>S. pimpinellifolium</i> accessions. The lowest cross-validation error is best K = 3. <b>Figure S2.</b><b> LD decay of the whole genome. </b>The pairwise LD through the whole genome was plotted. The red curve indicates non-linear regression. The dotted line indicates the fixed <i>r<sup>2</sup></i> on 0.1.<br> <b>Figure S3. 50 kb interval LD decay of each chromosome. </b>The pairwise LD of each chromosome was plotted individually and zoomed in to 50 kb interval to observe the fast LD decay. The red curves indicate non-linear regression. Black dotted lines indicate the fixed <i>r<sup>2</sup></i> on 0.1. <b>Figure S4. The local LD of each chromosome. </b>The LD of paired flanking sequencing units were plotted along each chromosome. The red dotted line indicates the baseline of <i>r<sup>2</sup></i>, and the orange line indicates the heterochromatic region. <b>Figure S5.</b><b> Pairwise isolation by distance of 98 accessions. </b>This plot presents the correlation between pairwise genetic distance and geographic distance. <b>Figure S6.</b><b> Pairwise isolation by distance of SolCAP meta-analysis. </b>This plot presents the correlation between pairwise genetic distance and geographic distance for the collection of 214 samples from the previous studies (Blanca <i>et al.</i> 2012, 2015; Sim <i>et al.</i> 2012a). <b>Figure S7.</b><b> The cross-validation error of SolCAP meta-analysis. </b>This figure shows the cross-validation of ADMIXTURE analysis for the SolCAP genotypes of the 214 <i>S. pimpinellifolium</i> accessions in the previous studies (Blanca <i>et al.</i> 2012, 2015; Sim <i>et al.</i> 2012a). The lowest cross-validation error shows best K = 15. <b>Figure S8. The genome patterns of 214 samples in SolCAP meta-analysis. </b>The labels on the top indicate accessions; the labels on the bottom indicate sample ID in this meta-analysis. <b>Figure S9.</b><b> The PCA of SolCAP meta-analysis. </b>A) The PCA plot of bi-allelic SNPs. B) The PCA plot after removing those SNPs of reverse-complement allele designation.<b>File S1. The analysis of isolation by distance. </b>The isolation by distance was performed for the 98 <i>S. pimpinellifolium</i> accessions with the RAD SNP dataset.<b>File S2. </b><b>The meta-analysis of <i>S. pimpinellifolium</i> SolCAP genotyping data. </b>We performed a meta-analysis of 214 <i>S. pimpinellifolium</i> accessions using SolCAP genotyping data downloaded from previous studies Sim <i>et al.</i> 2012a, Blanca <i>et al.</i> 2012, and Blanca <i>et al.</i> 2015.<br>
DOI: 10.6084/m9.figshare.7010474
title: Replication Package

description: Thank you for coming to our replication package page! In the zip folder, we include documents, scripts, and data to replicate our study.
DOI: 10.6084/m9.figshare.7010483.v1
title: Dataset for: Power analysis for multivariable Cox regression models

description: In power analysis for multivariable Cox regression models, variance of the estimated log-hazard ratio for the treatment effect is usually approximated by inverting the expected null information matrix. Because in many typical power analysis settings assumed true values of the hazard ratios are not necessarily close to unity, the accuracy of this approximation is not theoretically guaranteed. To address this problem, the null variance expression in power calculations can be replaced with one of alternative expressions derived under the assumed true value of the hazard ratio for the treatment effect. This approach is explored analytically and by simulations in the present paper. We consider several alternative variance expressions, and compare their performance to that of the traditional null variance expression. Theoretical analysis and simulations demonstrate that while the null variance expression performs well in many non-null settings, it can also be very inaccurate, substantially underestimating or overestimating the true variance in a wide range of realistic scenarios, particularly those where the numbers of treated and control subjects are very different and the true hazard ratio is not close to one. The alternative variance expressions have much better theoretical properties, confirmed in simulations. The most accurate of these expressions has a relatively simple form - it is the sum of inverse expected event counts under treatment and under control scaled up by a variance inflation factor.
DOI: 10.6084/m9.figshare.7010474.v1
title: Replication Package

description: ----- Overview -----Thank you for coming this appendix.This page provides a replication package to you for our each research question.<br>- (RQ1) To what degree are reviews connected?- (RQ2) Why are reviews being connected?- (RQ3) To what degree can connection types be automatically recovered?<br>----- Description -----<br>[package_rq1] This directory includes (i) scripts to crawl original review datasets via Gerrit Code Review system, (ii) scripts to generate a review graph for each software community, (iii) all review graphs of OpenStack, AOSP, Qt, Eclipse, and Libreoffice.<br>[package_rq2] This directory includes (i) scripts to obtain our sample datasets for manual classification and (ii) our coded samples of Nova and Neutron projects<br><br>[package_rq3] This directory includes (i) scripts to perform feature extraction, bootstrap sampling, hyperparameter optimization, model construction, and performance evaluation, and (ii) the data of results for automated classification in Nova and Neutron projects.<br><br>
DOI: 10.6084/m9.figshare.7010474.v3
title: Replication Package

description: ----- Overview -----Thank you for coming this appendix.This page provides a replication package to you for our each research question.<br>- (RQ1) To what degree are reviews connected?- (RQ2) Why are reviews being connected?- (RQ3) To what degree can connection types be automatically recovered?<br>----- Description -----<br>[package_rq1] This directory includes (i) scripts for RQ1 analysis and (ii) a review graph for each software community (OpenStack, AOSP, Qt, Eclipse, and Libreoffice)<br>[package_rq2] This directory includes the sample review links (.csv). The details of our codes are written in README file.<br><br>[package_rq3] This directory includes (i) scripts to perform feature extraction, bootstrap sampling, hyperparameter optimization, model construction, and performance evaluation, and (ii) the results of performance evaluation to Nova and Neutron projects.<br><br>
DOI: 10.6084/m9.figshare.7010474.v4
title: Replication Package

description: ----- Overview -----Thank you for coming this appendix.This page provides a replication package to you for our each research question.<br>- (RQ1) To what degree are reviews linked?- (RQ2) Why are reviews being linked?- (RQ3) To what degree can link categories be automatically recovered?- (RQ4) To what degree do linked reviews impact code review analytics?<br>----- Description -----<br>Each research question has scripts and data files. RQ1 includes the scripts that compute our quantitative analysis and extract review linkage graphs in our subject systems. RQ2 includes the data that shows our qualitative analysis results (i.e., manually analyzed links). RQ3 has a script that performs multi-class classification for five link categories. Finally, RQ4 has scripts that empirically analyze the impact of reviewer recommenders and outcome predictors.<br>[Environment] MongoDB, Python3.4 R 3.5.1<br>[RQ1]python3.4 linkTrendAnalysis_msr2019.py<br>Rscript plotLinkTrend_msr2019.Rpython3.4 statistics_numReviews_numReviewers.py<br>[RQ2]mr_fse2019.csv<br><br>[RQ3]python3.4 multi_Class_Classification_msr2019.py 1000<br>[RQ4]extract_comments_reviewers.ipynbpredictMultiCategories.ipynboverlappedReviewerAnalysis.ipynbcHRev_recommendation.ipynbrecommender_performance.ipynb<br>outcome_features_extraction.ipynbidenticalOutcomeRate.ipynboutcome_prediction.ipynb<br><br>
DOI: 10.6084/m9.figshare.7010438.v1
title: Supplementary Table 1

description: Disholcaspis specimens analyzed and their GenBank accession numbers
DOI: 10.6084/m9.figshare.7010363
title: Investigating Fractional Exhaled Nitric Oxide in Chronic Obstructive Pulmonary Disease (COPD) and Asthma-COPD Overlap (ACO): A Scoping Review

description: Chronic obstructive pulmonary disease (COPD) is the most common fixed airflow limitation. Individuals may present with the features of both asthma and COPD called asthma-COPD overlap (ACO) with more severity and worse health-related quality of life than COPD or asthma. One of the promising biomarkers that could be used in clinical practice to differentiate ACO from COPD is fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>). The role of Fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>) in COPD/ACO remains unknown. This scoping review aims to investigate the role of <i>F</i><sub>ENO</sub> measurement to differentiate COPD from ACO, to anticipate disease severity/progression and treatment response. A structured comprehensive literature search was performed in major databases including Medline, EMBASE, CINAHL, Cochrane Library, Web of Science, and BIOSIS from 2005 onwards. Thirty-eight studies were retrieved. Based on the synthesis of the reviewed literature, six themes emerged. Thirty-four articles covered more than one theme. From which, 24 articles were on modifying factors in <i>F</i><sub>ENO</sub> measurement, 18 on <i>F</i><sub>ENO</sub> in COPD compared with healthy subjects, and seven on <i>F</i><sub>ENO</sub> in ACO compared with COPD, 22 on <i>F</i><sub>ENO</sub> and disease severity/progression,12 on <i>F</i><sub>ENO</sub> and biomarkers, and eight on <i>F</i><sub>ENO</sub> and treatment response. <i>F</i><sub>ENO</sub> measurement cannot be used alone in the clinical settings of COPD patients. Although <i>F</i><sub>ENO</sub> level is higher in ACO patients than COPD-only, it is still unclear if there is a <i>F</i><sub>ENO</sub> cut-off that can be used to make the diagnosis of ACO and/or to guide therapy with inhaled corticosteroids/glucocorticoids in COPD patients.
DOI: 10.6084/m9.figshare.7010363.v2
title: Investigating Fractional Exhaled Nitric Oxide in Chronic Obstructive Pulmonary Disease (COPD) and Asthma-COPD Overlap (ACO): A Scoping Review

description: Chronic obstructive pulmonary disease (COPD) is the most common fixed airflow limitation. Individuals may present with the features of both asthma and COPD called asthma-COPD overlap (ACO) with more severity and worse health-related quality of life than COPD or asthma. One of the promising biomarkers that could be used in clinical practice to differentiate ACO from COPD is fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>). The role of Fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>) in COPD/ACO remains unknown. This scoping review aims to investigate the role of <i>F</i><sub>ENO</sub> measurement to differentiate COPD from ACO, to anticipate disease severity/progression and treatment response. A structured comprehensive literature search was performed in major databases including Medline, EMBASE, CINAHL, Cochrane Library, Web of Science, and BIOSIS from 2005 onwards. Thirty-eight studies were retrieved. Based on the synthesis of the reviewed literature, six themes emerged. Thirty-four articles covered more than one theme. From which, 24 articles were on modifying factors in <i>F</i><sub>ENO</sub> measurement, 18 on <i>F</i><sub>ENO</sub> in COPD compared with healthy subjects, and seven on <i>F</i><sub>ENO</sub> in ACO compared with COPD, 22 on <i>F</i><sub>ENO</sub> and disease severity/progression,12 on <i>F</i><sub>ENO</sub> and biomarkers, and eight on <i>F</i><sub>ENO</sub> and treatment response. <i>F</i><sub>ENO</sub> measurement cannot be used alone in the clinical settings of COPD patients. Although <i>F</i><sub>ENO</sub> level is higher in ACO patients than COPD-only, it is still unclear if there is a <i>F</i><sub>ENO</sub> cut-off that can be used to make the diagnosis of ACO and/or to guide therapy with inhaled corticosteroids/glucocorticoids in COPD patients.
DOI: 10.6084/m9.figshare.7010357
title: Trends in Self-Reporting of Marijuana Consumption in the United States

description: To adjust for underreporting of marijuana use, researchers multiply the proportion of individuals who reported using marijuana by a constant factor, such as the US Office of National Drug Control Policy’s 1.3. Although the current adjustments are simple, they do not account for changes in reporting over time. This article presents a novel way to explore relative changes in reporting from one survey to another simply by using data already available in a self-reported survey, the National Survey on Drug Use and Health. Using domain estimation to examine the stability in reported marijuana use by age 25 in individuals older than 25, this analysis provides estimates of the trends in marijuana reporting and standard errors, as long as the survey weights properly account for sampling variability. There was no significant evidence of an upward or downward trend in reporting changes from 1979 to 2016 for all birth cohorts, although there were significant differences in reporting between years and a slight downward trend in later years. These results suggest that individuals have become increasingly less willing to report their drug use in recent years, and thus the ONDCP likely underestimated the already drastic increase in use from 1992 to 2016.
DOI: 10.6084/m9.figshare.7010360
title: Controlling Sources of Inaccuracy in Stochastic Kriging

description: Scientists and engineers commonly use simulation models to study real systems for which actual experimentation is costly, difficult, or impossible. Many simulations are stochastic in the sense that repeated runs with the same input configuration will result in different outputs. For expensive or time-consuming simulations, stochastic kriging is commonly used to generate predictions for simulation model outputs subject to uncertainty due to both function approximation and stochastic variation. Here, we develop and justify a few guidelines for experimental design, which ensure accuracy of stochastic kriging emulators. We decompose error in stochastic kriging predictions into nominal, numeric, parameter estimation, and parameter estimation numeric components and provide means to control each in terms of properties of the underlying experimental design. The design properties implied for each source of error are weakly conflicting and broad principles are proposed. In brief, space-filling properties, “small fill distance” and “large separation distance,” should be balanced with replication at distinct input configurations, with number of replications depending on the relative magnitudes of stochastic and process variability. Nonstationarity implies higher input density in more active regions, while regression functions imply a balance with traditional design properties. A few examples are presented to illustrate the results. Supplementary materials providing proofs of the theoretical results and code for comparisons are available online.
DOI: 10.6084/m9.figshare.7010363.v1
title: Investigating Fractional Exhaled Nitric Oxide in Chronic Obstructive Pulmonary Disease (COPD) and Asthma-COPD Overlap (ACO): A Scoping Review

description: Chronic obstructive pulmonary disease (COPD) is the most common fixed airflow limitation. Individuals may present with the features of both asthma and COPD called asthma-COPD overlap (ACO) with more severity and worse health-related quality of life than COPD or asthma. One of the promising biomarkers that could be used in clinical practice to differentiate ACO from COPD is fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>). The role of Fractional exhaled nitric oxide (<i>F</i><sub>ENO</sub>) in COPD/ACO remains unknown. This scoping review aims to investigate the role of <i>F</i><sub>ENO</sub> measurement to differentiate COPD from ACO, to anticipate disease severity/progression and treatment response. A structured comprehensive literature search was performed in major databases including Medline, EMBASE, CINAHL, Cochrane Library, Web of Science, and BIOSIS from 2005 onwards. Thirty-eight studies were retrieved. Based on the synthesis of the reviewed literature, six themes emerged. Thirty-four articles covered more than one theme. From which, 24 articles were on modifying factors in <i>F</i><sub>ENO</sub> measurement, 18 on <i>F</i><sub>ENO</sub> in COPD compared with healthy subjects, and seven on <i>F</i><sub>ENO</sub> in ACO compared with COPD, 22 on <i>F</i><sub>ENO</sub> and disease severity/progression,12 on <i>F</i><sub>ENO</sub> and biomarkers, and eight on <i>F</i><sub>ENO</sub> and treatment response. <i>F</i><sub>ENO</sub> measurement cannot be used alone in the clinical settings of COPD patients. Although <i>F</i><sub>ENO</sub> level is higher in ACO patients than COPD-only, it is still unclear if there is a <i>F</i><sub>ENO</sub> cut-off that can be used to make the diagnosis of ACO and/or to guide therapy with inhaled corticosteroids/glucocorticoids in COPD patients.
DOI: 10.7284/107386
title: GNSS (Global Navigation Satellite System) data collected during research cruise EN503 using a Furuno GP-90D instrument system onboard the platform RV Endeavor
DOI: 10.6084/m9.figshare.7010360.v2
title: Controlling Sources of Inaccuracy in Stochastic Kriging

description: Scientists and engineers commonly use simulation models to study real systems for which actual experimentation is costly, difficult, or impossible. Many simulations are stochastic in the sense that repeated runs with the same input configuration will result in different outputs. For expensive or time-consuming simulations, stochastic kriging is commonly used to generate predictions for simulation model outputs subject to uncertainty due to both function approximation and stochastic variation. Here, we develop and justify a few guidelines for experimental design, which ensure accuracy of stochastic kriging emulators. We decompose error in stochastic kriging predictions into nominal, numeric, parameter estimation, and parameter estimation numeric components and provide means to control each in terms of properties of the underlying experimental design. The design properties implied for each source of error are weakly conflicting and broad principles are proposed. In brief, space-filling properties, “small fill distance” and “large separation distance,” should be balanced with replication at distinct input configurations, with number of replications depending on the relative magnitudes of stochastic and process variability. Nonstationarity implies higher input density in more active regions, while regression functions imply a balance with traditional design properties. A few examples are presented to illustrate the results. Supplementary materials providing proofs of the theoretical results and code for comparisons are available online.
DOI: 10.6084/m9.figshare.7010357.v2
title: Trends in Self-Reporting of Marijuana Consumption in the United States

description: To adjust for underreporting of marijuana use, researchers multiply the proportion of individuals who reported using marijuana by a constant factor, such as the US Office of National Drug Control Policy’s 1.3. Although the current adjustments are simple, they do not account for changes in reporting over time. This article presents a novel way to explore relative changes in reporting from one survey to another simply by using data already available in a self-reported survey, the National Survey on Drug Use and Health. Using domain estimation to examine the stability in reported marijuana use by age 25 in individuals older than 25, this analysis provides estimates of the trends in marijuana reporting and standard errors, as long as the survey weights properly account for sampling variability. There was no significant evidence of an upward or downward trend in reporting changes from 1979 to 2016 for all birth cohorts, although there were significant differences in reporting between years and a slight downward trend in later years. These results suggest that individuals have become increasingly less willing to report their drug use in recent years, and thus the ONDCP likely underestimated the already drastic increase in use from 1992 to 2016.
DOI: 10.6084/m9.figshare.7010360.v1
title: Controlling Sources of Inaccuracy in Stochastic Kriging

description: Scientists and engineers commonly use simulation models to study real systems for which actual experimentation is costly, difficult, or impossible. Many simulations are stochastic in the sense that repeated runs with the same input configuration will result in different outputs. For expensive or time-consuming simulations, stochastic kriging is commonly used to generate predictions for simulation model outputs subject to uncertainty due to both function approximation and stochastic variation. Here, we develop and justify a few guidelines for experimental design, which ensure accuracy of stochastic kriging emulators. We decompose error in stochastic kriging predictions into nominal, numeric, parameter estimation, and parameter estimation numeric components and provide means to control each in terms of properties of the underlying experimental design. The design properties implied for each source of error are weakly conflicting and broad principles are proposed. In brief, space-filling properties, “small fill distance” and “large separation distance,” should be balanced with replication at distinct input configurations, with number of replications depending on the relative magnitudes of stochastic and process variability. Nonstationarity implies higher input density in more active regions, while regression functions imply a balance with traditional design properties. A few examples are presented to illustrate the results. Supplementary materials providing proofs of the theoretical results and code for comparisons are available online.