cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12105759.v1
title: BMI-based Non-quadratic Controller Design for Polytopic-LPV Systems

description: <br> The supported data for Fig. 1 in paper "BMI-based Non-quadratic Controller Design for Polytopic-LPV Systems” which proves the superiority of the proposed BMI approach <br>
DOI: 10.25380/iastate.12103989
title: The effect of a liquid phase on force distribution during deformation in a granular system dataset

description: This dataset includes raw images from granular and two-phase experiments, as well as force data from each individual experiment. More information about methods and materials can be accessed in the README.<br>Two-phase systems, where one phase is solid and the other fluid, are widespread in nature. Examples include reservoir rocks holding vital fluids like water or petroleum, slurries of partially crystallized magmas, the semi-brittle middle crust, and fluids migrating along faults filled with fault gouge. Previous studies of two-phase systems have shown that the weak phase plays an important role on deformation localization and dynamics. Here, we investigate the influence of a weak phase on force distribution in a granular media during simple shear. We use photoelastic polyurethane discs as the granular or strong phase and a linear-viscous silicone as the weak phase. The photoelastic property of the discs allows for direct observation and measurement of force magnitude and distribution. We compare the two-phase experiments to granular experiments without silicone. The addition and percentage of the weak phase has a strong impact on the force distribution and the overall force chain orientations.
DOI: 10.25380/iastate.12103989.v1
title: The effect of a liquid phase on force distribution during deformation in a granular system dataset

description: This dataset includes raw images from granular and two-phase experiments, as well as force data from each individual experiment. More information about methods and materials can be accessed in the README.<br>Two-phase systems, where one phase is solid and the other fluid, are widespread in nature. Examples include reservoir rocks holding vital fluids like water or petroleum, slurries of partially crystallized magmas, the semi-brittle middle crust, and fluids migrating along faults filled with fault gouge. Previous studies of two-phase systems have shown that the weak phase plays an important role on deformation localization and dynamics. Here, we investigate the influence of a weak phase on force distribution in a granular media during simple shear. We use photoelastic polyurethane discs as the granular or strong phase and a linear-viscous silicone as the weak phase. The photoelastic property of the discs allows for direct observation and measurement of force magnitude and distribution. We compare the two-phase experiments to granular experiments without silicone. The addition and percentage of the weak phase has a strong impact on the force distribution and the overall force chain orientations.
DOI: 10.6084/m9.figshare.12103617
title: Dataset: The Influence of Chronotype and Grit on Lifestyle and Physical Activity

description: <b>Demographics</b><br>This dataset contains data collected from 431 first-semester students enrolled in Oral Roberts University’s success course entitled GEN 150 - Introduction to Whole Person Education during the Fall semester of 2019.<br><b>Variables</b><b><br></b><b>Age</b>. The age of the student, in years, on December 1, 2019.<br><b>Grit</b>. A measure of each student’s “grit” using Duckworth et al.’s (2007) standard 12-question instrument.<br><b>Lifestyle</b>. Each student’s lifestyle was measured using a 35-question lifestyle survey that has items that cover aspects of personal health care, drugs and alcohol use, physical fitness, psychological condition, spiritual condition, personal behavior, and nutrition.<br><b>MEQ</b>. A measure of each student’s chronotype using the standard 19-item Morningness-Eveningness Questionnaire.<br><b>Sex</b>. The sex of the student, encoded as follows: “0” female, “1” male. Data were collected from 262 females (61%) and 169 males (39%).<br><b>Steps</b>. The average number of steps that students took per day as recorded by the Fitbit device.<br>
DOI: 10.6084/m9.figshare.12103422
title: data.tar.gz

description: Data of CAPTCHA<br>
DOI: 10.6084/m9.figshare.12103416
title: Mathematica codes

description: The zip file contains the article, Mathematica codes and data that take a long time to calculate.<br>
DOI: 10.6084/m9.figshare.12103422.v1
title: data.tar.gz

description: Data of CAPTCHA<br>
DOI: 10.6084/m9.figshare.12103416.v1
title: Mathematica codes

description: The zip file contains the article, Mathematica codes and data that take a long time to calculate.<br>
DOI: 10.6084/m9.figshare.12103275
title: Raw Data and Metrics from the study titled: A Precision Teaching framework for improving mathematical skills of students with intellectual and developmental disabilities.

description: This excel file includes the raw data and metrics for all 16 participants that took part in the study. It also includes the fluency aim calculator and the raw data from the calculation of the Non-Overlap of All Pairs (NAP).
DOI: 10.6084/m9.figshare.12103275.v1
title: Raw Data and Metrics from the study titled: A Precision Teaching framework for improving mathematical skills of students with intellectual and developmental disabilities.

description: This excel file includes the raw data and metrics for all 16 participants that took part in the study. It also includes the fluency aim calculator and the raw data from the calculation of the Non-Overlap of All Pairs (NAP).
DOI: 10.6084/m9.figshare.12103161
title: Randomization Tests for Equality in Dependence Structure

description: We develop a new statistical procedure to test whether the dependence structure is identical between two groups. Rather than relying on a single index such as Pearson’s correlation coefficient or Kendall’s <i>τ</i>, we consider the entire dependence structure by investigating the dependence functions (copulas). The critical values are obtained by a modified randomization procedure designed to exploit asymptotic group invariance conditions. Implementation of the test is intuitive and simple, and does not require any specification of a tuning parameter or weight function. At the same time, the test exhibits excellent finite sample performance, with the null rejection rates almost equal to the nominal level even when the sample size is extremely small. Two empirical applications concerning the dependence between income and consumption, and the Brexit effect on European financial market integration are provided.
DOI: 10.6084/m9.figshare.12103164
title: A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models

description: To estimate causal effects from observational data, an applied researcher must impose beliefs. The instrumental variables exclusion restriction, for example, represents the belief that the instrument has no direct effect on the outcome of interest. Yet beliefs about instrument validity do not exist in isolation. Applied researchers often discuss the likely direction of selection and the potential for measurement error in their articles but lack formal tools for incorporating this information into their analyses. Failing to use all relevant information not only leaves money on the table; it runs the risk of leading to a contradiction in which one holds mutually incompatible beliefs about the problem at hand. To address these issues, we first characterize the joint restrictions relating instrument invalidity, treatment endogeneity, and non-differential measurement error in a workhorse linear model, showing how beliefs over these three dimensions are mutually constrained by each other and the data. Using this information, we propose a Bayesian framework to help researchers elicit their beliefs, incorporate them into estimation, and ensure their mutual coherence. We conclude by illustrating our framework in a number of examples drawn from the empirical microeconomics literature.
DOI: 10.6084/m9.figshare.12103149
title: Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables

description: <b>This article studies nonparametric series estimation and inference for the effect of a single variable of interest <i>x</i> on an outcome <i>y</i> in the presence of potentially high-dimensional conditioning variables <i>z</i>. The context is an additively separable model</b>E[y|x,z]=g0(x)+h0(z)<b>. The model is high-dimensional in the sense that the series of approximating functions for</b>h0(z)<b>can have more terms than the sample size, thereby allowing <i>z</i> potentially to have very many measured characteristics. The model is required to be approximately sparse:</b>h0(z)<b>can be approximated using only a small subset of series terms whose identities are unknown. This article proposes an estimation and inference method for</b>g0(x)<b>called <i>Post-Nonparametric Double Selection</i>, which is a generalization of <i>Post-Double Selection</i>. Rates of convergence and asymptotic normality for the estimator are derived and hold over a large class of sparse data-generating processes. A simulation study illustrates finite sample estimation properties of the proposed estimator and coverage properties of the corresponding confidence intervals. Finally, an empirical application to college admissions policy demonstrates the practical implementation of the proposed method.</b>
DOI: 10.6084/m9.figshare.12103161.v1
title: Randomization Tests for Equality in Dependence Structure

description: We develop a new statistical procedure to test whether the dependence structure is identical between two groups. Rather than relying on a single index such as Pearson’s correlation coefficient or Kendall’s <i>τ</i>, we consider the entire dependence structure by investigating the dependence functions (copulas). The critical values are obtained by a modified randomization procedure designed to exploit asymptotic group invariance conditions. Implementation of the test is intuitive and simple, and does not require any specification of a tuning parameter or weight function. At the same time, the test exhibits excellent finite sample performance, with the null rejection rates almost equal to the nominal level even when the sample size is extremely small. Two empirical applications concerning the dependence between income and consumption, and the Brexit effect on European financial market integration are provided.
DOI: 10.6084/m9.figshare.12103164.v2
title: A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models

description: To estimate causal effects from observational data, an applied researcher must impose beliefs. The instrumental variables exclusion restriction, for example, represents the belief that the instrument has no direct effect on the outcome of interest. Yet beliefs about instrument validity do not exist in isolation. Applied researchers often discuss the likely direction of selection and the potential for measurement error in their articles but lack formal tools for incorporating this information into their analyses. Failing to use all relevant information not only leaves money on the table; it runs the risk of leading to a contradiction in which one holds mutually incompatible beliefs about the problem at hand. To address these issues, we first characterize the joint restrictions relating instrument invalidity, treatment endogeneity, and non-differential measurement error in a workhorse linear model, showing how beliefs over these three dimensions are mutually constrained by each other and the data. Using this information, we propose a Bayesian framework to help researchers elicit their beliefs, incorporate them into estimation, and ensure their mutual coherence. We conclude by illustrating our framework in a number of examples drawn from the empirical microeconomics literature.
DOI: 10.6084/m9.figshare.12103164.v1
title: A Framework for Eliciting, Incorporating, and Disciplining Identification Beliefs in Linear Models*†

description: To estimate causal effects from observational data, an applied researcher must impose beliefs. The instrumental variables exclusion restriction, for example, represents the belief that the instrument has no direct effect on the outcome of interest. Yet beliefs about instrument validity do not exist in isolation. Applied researchers often discuss the likely direction of selection and the potential for measurement error in their papers but lack formal tools for incorporating this information into their analyses. Failing to use all relevant information not only leaves money on the table; it runs the risk of leading to a contradiction in which one holds mutually incompatible beliefs about the problem at hand. To address these issues, we first characterize the joint restrictions relating instrument invalidity, treatment endogeneity, and non-differential measurement error in a workhorse linear model, showing how beliefs over these three dimensions are mutually constrained by each other and the data. Using this information, we propose a Bayesian framework to help researchers elicit their beliefs, incorporate them into estimation, and ensure their mutual coherence. We conclude by illustrating our framework in a number of examples drawn from the empirical microeconomics literature. Keywords: Partial identification, Beliefs, Instrumental variables, Measurement error, Bayesian econometrics
DOI: 10.6084/m9.figshare.12103149.v1
title: Inference in Additively Separable Models with a High-Dimensional Set of Conditioning Variables

description: This paper studies nonparametric series estimation and inference for the effect of a single variable of interest <i>x</i> on an outcome <i>y</i> in the presence of potentially high-dimensional conditioning variables <i>z</i>. The context is an additively separable model E[y|x,z]=g0(x)+h0(z). The model is high-dimensional in the sense that the series of approximating functions for h0(z) can have more terms than the sample size, thereby allowing <i>z</i> potentially to have very many measured characteristics. The model is required to be approximately sparse: h0(z) can be approximated using only a small subset of series terms whose identities are unknown. This paper proposes an estimation and inference method for g0(x) called <i>Post-Nonparametric Double Selection</i>, which is a generalization of <i>Post-Double Selection</i>. Rates of convergence and asymptotic normality for the estimator are derived and hold over a large class of sparse data generating processes. A simulation study illustrates finite sample estimation properties of the proposed estimator and coverage properties of the corresponding confidence intervals. Finally, an empirical application to college admissions policy demonstrates the practical implementation of the proposed method.
DOI: 10.6084/m9.figshare.12103149.v2
title: Inference in Additively Separable Models With a High-Dimensional Set of Conditioning Variables

description: <b>This article studies nonparametric series estimation and inference for the effect of a single variable of interest <i>x</i> on an outcome <i>y</i> in the presence of potentially high-dimensional conditioning variables <i>z</i>. The context is an additively separable model</b>E[y|x,z]=g0(x)+h0(z)<b>. The model is high-dimensional in the sense that the series of approximating functions for</b>h0(z)<b>can have more terms than the sample size, thereby allowing <i>z</i> potentially to have very many measured characteristics. The model is required to be approximately sparse:</b>h0(z)<b>can be approximated using only a small subset of series terms whose identities are unknown. This article proposes an estimation and inference method for</b>g0(x)<b>called <i>Post-Nonparametric Double Selection</i>, which is a generalization of <i>Post-Double Selection</i>. Rates of convergence and asymptotic normality for the estimator are derived and hold over a large class of sparse data-generating processes. A simulation study illustrates finite sample estimation properties of the proposed estimator and coverage properties of the corresponding confidence intervals. Finally, an empirical application to college admissions policy demonstrates the practical implementation of the proposed method.</b>
DOI: 10.6084/m9.figshare.12102954
title: Supplementary Material for: Mental Health and Psychosocial Problems of Medical Health Workers during the COVID-19 Epidemic in China

description: <b><i>Objective:</i></b> We explored whether medical health workers had more psychosocial problems than nonmedical health workers during the COVID-19 outbreak. <b><i>Methods:</i></b> An online survey was run from February 19 to March 6, 2020; a total of 2,182 Chinese subjects participated. Mental health variables were assessed via the Insomnia Severity Index (ISI), the Symptom Check List-revised (SCL-90-R), and the Patient Health Questionnaire-4 (PHQ-4), which included a 2-item anxiety scale and a 2-item depression scale (PHQ-2). <b><i>Results:</i></b> Compared with nonmedical health workers (<i>n</i> = 1,255), medical health workers (<i>n</i> = 927) had a higher prevalence of insomnia (38.4 vs. 30.5%, <i>p</i> &lt; 0.01), anxiety (13.0 vs. 8.5%, <i>p</i> &lt; 0.01), depression (12.2 vs. 9.5%; <i>p</i>&lt; 0.04), somatization (1.6 vs. 0.4%; <i>p</i> &lt; 0.01), and obsessive-compulsive symptoms (5.3 vs. 2.2%; <i>p</i> &lt; 0.01). They also had higher total scores of ISI, GAD-2, PHQ-2, and SCL-90-R obsessive-compulsive symptoms (<i>p</i> ≤ 0.01). Among medical health workers, having organic disease was an independent factor for insomnia, anxiety, depression, somatization, and obsessive-compulsive symptoms (<i>p</i> &lt; 0.05 or 0.01). Living in rural areas, being female, and being at risk of contact with COVID-19 patients were the most common risk factors for insomnia, anxiety, obsessive-compulsive symptoms, and depression (<i>p</i> &lt; 0.01 or 0.05). Among nonmedical health workers, having organic disease was a risk factor for insomnia, depression, and obsessive-compulsive symptoms (<i>p</i> &lt; 0.01 or 0.05). <b><i>Conclusions:</i></b> During the COVID-19 outbreak, medical health workers had psychosocial problems and risk factors for developing them. They were in need of attention and recovery programs.
DOI: 10.6084/m9.figshare.12102795
title: TCGA full RNA-seq matrix

description: Data frame containing RNA-seq data for all TCGA samples<br>
DOI: 10.6084/m9.figshare.12102777
title: pQTL GWAS association data for ACE2 in plasma from INTERVAL study

description: Genetic association estimates with circulating plasma ACE2 levels were obtained in a subcohort of 4,998 blood donors enrolled in the INTERVAL study. Plasma ACE2 levels were measured using a multiplex proximity extension immunoassay (Cardiovascular 2 panel, Olink Bioscience, Uppsala, Sweden). A total of 4,947 samples passed quality control. The data were pre-processed using standard Olink workflows including applying median centring normalization across plates, where the median is centred to the overall median for all plates, followed by log2 transformation to provide normalized protein levels (NPX). NPX values were regressed on age, sex, plate, time from blood draw to processing (in days), and season. The residuals were then rank-inverse normalized. Genotype data was processed as described previously. Genome-wide pQTL analysis was performed by linear regression of the rank-inverse normalized residuals on genotype in SNPTEST, with the first three components of multi-dimensional scaling as covariates to adjust for ancestry. <br>
DOI: 10.6084/m9.figshare.12102783
title: Virtual Screening by Acellera

description: Virtual Screening campaign against main protease of SARS-CoV-2 using rDock and pharmacophoric filtering.<br>
DOI: 10.6084/m9.figshare.12102777.v1
title: pQTL GWAS association data for ACE2 in plasma from INTERVAL study

description: Genetic association estimates with circulating plasma ACE2 levels were obtained in a subcohort of 4,998 blood donors enrolled in the INTERVAL study. Plasma ACE2 levels were measured using a multiplex proximity extension immunoassay (Cardiovascular 2 panel, Olink Bioscience, Uppsala, Sweden). A total of 4,947 samples passed quality control. The data were pre-processed using standard Olink workflows including applying median centring normalization across plates, where the median is centred to the overall median for all plates, followed by log2 transformation to provide normalized protein levels (NPX). NPX values were regressed on age, sex, plate, time from blood draw to processing (in days), and season. The residuals were then rank-inverse normalized. Genotype data was processed as described previously. Genome-wide pQTL analysis was performed by linear regression of the rank-inverse normalized residuals on genotype in SNPTEST, with the first three components of multi-dimensional scaling as covariates to adjust for ancestry. <br>
DOI: 10.6084/m9.figshare.12102753
title: Test.rar

description: Data for the submitted paper titled xxx
DOI: 10.6084/m9.figshare.12102783.v2
title: Virtual Screening by Acellera

description: Virtual Screening campaign against main protease of SARS-CoV-2 using rDock and pharmacophoric filtering.<br>
DOI: 10.6084/m9.figshare.12102753.v1
title: Test.rar

description: Data for the submitted paper titled xxx
DOI: 10.6084/m9.figshare.12102783.v1
title: Virtual Screening by Acellera

description: Best pose of each compound. <br>
DOI: 10.6084/m9.figshare.12102711
title: eQTL GWAS association data for ACE2 and TMPRSS2 in lung tissue from Lung eQTL Consortium

description: Lung eQTL Consortium genetic association estimates were obtained in 1,038 individuals of European ancestry. Tissue samples were obtained at three different institutions: University of British Columbia, Laval University, and University of Groningen. Genome-wide eQTL analysis was performed for the expression for ACE2 using probe 100134205_TGI_at and two non-control probes for TMPRSS2, 100130004_TGI_at and 100157336_TGI_at. All participants were genotyped using the Illumina Human 1M Duo BearChip and the genotypes were imputed using the Haplotype Reference Consortium reference panel. Expression values were first standardized for age, sex, and smoking status using robust linear regression. Genetic associations were estimated in each cohort separately using a linear additive genetic model. The estimates were combined across cohorts using an inverse-variance weighted model with fixed-effects.
DOI: 10.6084/m9.figshare.12102711.v1
title: eQTL GWAS association data for ACE2 and TMPRSS2 in lung tissue from Lung eQTL Consortium

description: Lung eQTL Consortium genetic association estimates were obtained in 1,038 individuals of European ancestry. Tissue samples were obtained at three different institutions: University of British Columbia, Laval University, and University of Groningen. Genome-wide eQTL analysis was performed for the expression for ACE2 using probe 100134205_TGI_at and two non-control probes for TMPRSS2, 100130004_TGI_at and 100157336_TGI_at. All participants were genotyped using the Illumina Human 1M Duo BearChip and the genotypes were imputed using the Haplotype Reference Consortium reference panel. Expression values were first standardized for age, sex, and smoking status using robust linear regression. Genetic associations were estimated in each cohort separately using a linear additive genetic model. The estimates were combined across cohorts using an inverse-variance weighted model with fixed-effects.