cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14573742
title: Screened mammalian species and genome assemblies

description: Screened mammalian species and genome assemblies
DOI: 10.6084/m9.figshare.14573742.v2
title: Screened mammalian species and genome assemblies

description: Screened mammalian species and genome assemblies
DOI: 10.6084/m9.figshare.14573742.v1
title: Screened mammalian species and genome assemblies

description: Screened mammalian species and genome assemblies
DOI: 10.6084/m9.figshare.14607531
title: R Script for Cell Count and Geochemistry Plots

description: R script used to make plots of averaged cell count and geochemistry data from microcosm experiments.
DOI: 10.6084/m9.figshare.14607531.v1
title: R Script for Cell Count and Geochemistry Plots

description: R script used to make plots of averaged cell count and geochemistry data from microcosm experiments.
DOI: 10.6084/m9.figshare.14669784
title: Final Timepoint 25 Most Abundant OTUs - Counts and Taxonomy

description: File showing the normalized abundance and taxonomy classifications of the 25 most abundant OTUs in final time point microcosm samples.
DOI: 10.6084/m9.figshare.14669784.v1
title: Final Timepoint 25 Most Abundant OTUs - Counts and Taxonomy

description: File showing the normalized abundance and taxonomy classifications of the 25 most abundant OTUs in final time point microcosm samples.
DOI: 10.6084/m9.figshare.14669802
title: Adnexal torsion raw data

description: This data is raw data for adnexal torsion.
DOI: 10.6084/m9.figshare.14669802.v1
title: Adnexal torsion raw data

description: This data is raw data for adnexal torsion.
DOI: 10.6084/m9.figshare.14661591
title: Data_radiocapitellar_incongruity.xlsx

description: <b>Measurement of radiocapitellar incongruity in patients with lateral epicondylitis. (CET, Common Extensor Tendon; LCLC, Lateral Collateral Ligamentous Complex; OCD, Osteochondral lesion; BM, Bone Marrow Signal Change; PT, Posterior Translation of the Radial Head; PLRI, Posterolateral Rotatory Instability)</b>
DOI: 10.6084/m9.figshare.14661591.v1
title: Data_radiocapitellar_incongruity.xlsx

description: <b>Measurement of radiocapitellar incongruity in patients with lateral epicondylitis. (CET, Common Extensor Tendon; LCLC, Lateral Collateral Ligamentous Complex; OCD, Osteochondral lesion; BM, Bone Marrow Signal Change; PT, Posterior Translation of the Radial Head; PLRI, Posterolateral Rotatory Instability)</b>
DOI: 10.6084/m9.figshare.14669817.v1
title: SRR3414636.uniq.sorted.bam

description: RNAseq
DOI: 10.6084/m9.figshare.14669817
title: SRR3414636.uniq.sorted.bam

description: RNAseq
DOI: 10.6084/m9.figshare.14669832.v1
title: SRR3414636.uniq.sorted.bam.bai

description: RNA SEQ
DOI: 10.6084/m9.figshare.14669832
title: SRR3414636.uniq.sorted.bam.bai

description: RNA SEQ
DOI: 10.6084/m9.figshare.14669889.v1
title: Disentangling Cuscuta identification in Brazil: a first taxonomic contribution to the northeast region species – Supplementay Material

description: <b>Table S1 – </b>Database. The table displays data of the specimens identified as herbarium (coding and barcode), collection (collector, number, date, locality), label's observations and phenology. <br>
DOI: 10.6084/m9.figshare.14669889
title: Disentangling Cuscuta identification in Brazil: a first taxonomic contribution to the northeast region species – Supplementay Material

description: <b>Table S1 – </b>Database. The table displays data of the specimens identified as herbarium (coding and barcode), collection (collector, number, date, locality), label's observations and phenology. <br>
DOI: 10.6084/m9.figshare.14669907
title: FMEA Result.csv

description: The results of the FMEA Questionnaire assess the risk of failure of the solid waste management process
DOI: 10.6084/m9.figshare.14669916
title: IBD patients and healthy controls

description: the current exercise situation of CD patients, UC patients and healthy controls
DOI: 10.6084/m9.figshare.14669916.v1
title: IBD patients and healthy controls

description: the current exercise situation of CD patients, UC patients and healthy controls
DOI: 10.6084/m9.figshare.12984291
title: Warp Bridge Sampling: The Next Generation

description: Bridge sampling is an effective Monte Carlo (MC) method for estimating the ratio of normalizing constants of two probability densities, a routine computational problem in statistics, physics, chemistry, and other fields. The MC error of the bridge sampling estimator is determined by the amount of overlap between the two densities. In the case of unimodal densities, Warp-I, II, and III transformations are effective for increasing the initial overlap, but they are less so for multimodal densities. This article introduces Warp-U transformations that aim to transform multimodal densities into unimodal ones (hence “U”) without altering their normalizing constants. The construction of a Warp-U transformation starts with a normal (or other convenient) mixture distribution ϕmix that has reasonable overlap with the target density <i>p</i>, whose normalizing constant is unknown. The stochastic transformation that maps ϕmix back to its generating distribution N(0,1) is then applied to <i>p</i> yielding its Warp-U version, which we denote p˜. Typically, p˜ is unimodal and has substantially increased overlap with ϕ. Furthermore, we prove that the overlap between p˜ and N(0,1) is guaranteed to be no less than the overlap between <i>p</i> and ϕmix, in terms of any <i>f</i>-divergence. We propose a computationally efficient method to find an appropriate ϕmix, and a simple but effective approach to remove the bias which results from estimating the normalizing constant and fitting ϕmix with the same data. We illustrate our findings using 10 and 50 dimensional highly irregular multimodal densities, and demonstrate how Warp-U sampling can be used to improve the final estimation step of the Generalized Wang–Landau algorithm, a powerful sampling and estimation approach. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12984291.v1
title: Warp Bridge Sampling: The Next Generation

description: Bridge sampling is an effective Monte Carlo (MC) method for estimating the ratio of normalizing constants of two probability densities, a routine computational problem in statistics, physics, chemistry, and other fields. The MC error of the bridge sampling estimator is determined by the amount of overlap between the two densities. In the case of unimodal densities, Warp-I, II, and III transformations are effective for increasing the initial overlap, but they are less so for multimodal densities. This article introduces Warp-U transformations that aim to transform multimodal densities into unimodal ones (hence “U”) without altering their normalizing constants. The construction of a Warp-U transformation starts with a normal (or other convenient) mixture distribution ϕmix that has reasonable overlap with the target density <i>p</i>, whose normalizing constant is unknown. The stochastic transformation that maps ϕmix back to its generating distribution N(0,1) is then applied to <i>p</i> yielding its Warp-U version, which we denote p˜. Typically, p˜ is unimodal and has substantially increased overlap with ϕ. Furthermore, we prove that the overlap between p˜ and N(0,1) is guaranteed to be no less than the overlap between <i>p</i> and ϕmix, in terms of any <i>f</i>-divergence. We propose a computationally efficient method to find an appropriate ϕmix, and a simple but effective approach to remove the bias which results from estimating the normalizing constant and fitting ϕmix with the same data. We illustrate our findings using 10 and 50 dimensional highly irregular multimodal densities, and demonstrate how Warp-U sampling can be used to improve the final estimation step of the Generalized Wang–Landau algorithm, a powerful sampling and estimation approach. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13360509.v2
title: Constrained Functional Regression of National Forest Inventory Data Over Time Using Remote Sensing Observations

description: The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United States. We specifically focus on a 230,400 ha region in north-central Wisconsin between 2003 and 2012. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures that we fill in first using a spatiotemporal model. Subsequently, we use the complete imagery as functional predictors in a two-component mixture model to capture the spatial variation in yearly average live tree basal area, an attribute of interest measured on field plots. We further modify the regression equation to accommodate a biophysical constraint on how plot-level live tree basal area can change from one year to the next. Findings from our analysis, represented with a series of maps, match known spatial patterns across the landscape. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.13360509
title: Constrained Functional Regression of National Forest Inventory Data Over Time Using Remote Sensing Observations

description: The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United States. We specifically focus on a 230,400 ha region in north-central Wisconsin between 2003 and 2012. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures that we fill in first using a spatiotemporal model. Subsequently, we use the complete imagery as functional predictors in a two-component mixture model to capture the spatial variation in yearly average live tree basal area, an attribute of interest measured on field plots. We further modify the regression equation to accommodate a biophysical constraint on how plot-level live tree basal area can change from one year to the next. Findings from our analysis, represented with a series of maps, match known spatial patterns across the landscape. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.13360509.v1
title: Constrained Functional Regression of National Forest Inventory Data over Time Using Remote Sensing Observations

description: The USDA Forest Service uses satellite imagery, along with a sample of national forest inventory field plots, to monitor and predict changes in forest conditions over time throughout the United States. We specifically focus on a 230, 400 hectare region in north-central Wisconsin between 2003-2012. The auxiliary data from the satellite imagery of this region are relatively dense in space and time, and can be used to learn how forest conditions changed over that decade. However, these records have a significant proportion of missing values due to weather conditions and system failures that we fill in first using a spatiotemporal model. Subsequently, we use the complete imagery as functional predictors in a two-component mixture model to capture the spatial variation in yearly average live tree basal area, an attribute of interest measured on field plots. We further modify the regression equation to accommodate a biophysical constraint on how plot-level live tree basal area can change from one year to the next. Findings from our analysis, represented with a series of maps, match known spatial patterns across the landscape. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.6084/m9.figshare.13135798.v1
title: Robust Post-Matching Inference

description: Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces the dependence on parametric modeling assumptions. In current empirical practice, however, the matching step is often ignored in the calculation of standard errors and confidence intervals. In this article, we show that ignoring the matching step results in asymptotically valid standard errors if matching is done without replacement and the regression model is correctly specified relative to the population regression function of the outcome variable on the treatment variable and <i>all</i> the covariates used for matching. However, standard errors that ignore the matching step are not valid if matching is conducted with replacement or, more crucially, if the second step regression model is misspecified in the sense indicated above. Moreover, correct specification of the regression model is not required for consistent estimation of treatment effects with matched data. We show that two easily implementable alternatives produce approximations to the distribution of the post-matching estimator that are robust to misspecification. A simulation study and an empirical example demonstrate the empirical relevance of our results. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13135798
title: Robust Post-Matching Inference

description: Nearest-neighbor matching is a popular nonparametric tool to create balance between treatment and control groups in observational studies. As a preprocessing step before regression, matching reduces the dependence on parametric modeling assumptions. In current empirical practice, however, the matching step is often ignored in the calculation of standard errors and confidence intervals. In this article, we show that ignoring the matching step results in asymptotically valid standard errors if matching is done without replacement and the regression model is correctly specified relative to the population regression function of the outcome variable on the treatment variable and <i>all</i> the covariates used for matching. However, standard errors that ignore the matching step are not valid if matching is conducted with replacement or, more crucially, if the second step regression model is misspecified in the sense indicated above. Moreover, correct specification of the regression model is not required for consistent estimation of treatment effects with matched data. We show that two easily implementable alternatives produce approximations to the distribution of the post-matching estimator that are robust to misspecification. A simulation study and an empirical example demonstrate the empirical relevance of our results. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13527403.v1
title: Discovering Heterogeneous Exposure Effects Using Randomization Inference in Air Pollution Studies

description: Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this paper, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to assess discovered heterogeneous effects. Also, we develop a sensitivity analysis method to assess the robustness of the conclusions to unmeasured confounding bias. Via simulation studies and theoretical arguments, we demonstrate that hypothesis testing focusing on the discovered subgroups can substantially increase statistical power to detect heterogeneity of the exposure effects. We apply the proposed denovo method to the data of 1,612,414 Medicare beneficiaries in the New England region in the United States for the period 2000 to 2006. We find that seniors aged between 81-85 with low income and seniors aged 85 and above have statistically significant greater causal effects of long-term exposure to PM2.5 on 5-year mortality rate compared to the population mean.
DOI: 10.6084/m9.figshare.12851262.v2
title: Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records

description: Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient’s health status influences when and what data are recorded, generating sampling bias in the collected data. In this article, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851262
title: Recurrent Events Analysis With Data Collected at Informative Clinical Visits in Electronic Health Records

description: Although increasingly used as a data resource for assembling cohorts, electronic health records (EHRs) pose many analytic challenges. In particular, a patient’s health status influences when and what data are recorded, generating sampling bias in the collected data. In this article, we consider recurrent event analysis using EHR data. Conventional regression methods for event risk analysis usually require the values of covariates to be observed throughout the follow-up period. In EHR databases, time-dependent covariates are intermittently measured during clinical visits, and the timing of these visits is informative in the sense that it depends on the disease course. Simple methods, such as the last-observation-carried-forward approach, can lead to biased estimation. On the other hand, complex joint models require additional assumptions on the covariate process and cannot be easily extended to handle multiple longitudinal predictors. By incorporating sampling weights derived from estimating the observation time process, we develop a novel estimation procedure based on inverse-rate-weighting and kernel-smoothing for the semiparametric proportional rate model of recurrent events. The proposed methods do not require model specifications for the covariate processes and can easily handle multiple time-dependent covariates. Our methods are applied to a kidney transplant study for illustration. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12404483.v2
title: Embedding learning

description: Numerical embedding has become one standard technique for processing and analyzing unstructured data that cannot be expressed in a predefined fashion. It stores the main characteristics of data by mapping it onto a numerical vector. An embedding is often unsupervised and constructed by transfer learning from large-scale unannotated data. Given an embedding, a downstream learning method, referred to as a two-stage method, is applicable to unstructured data. In this article, we introduce a novel framework of embedding learning to deliver a higher learning accuracy than the two-stage method while identifying an optimal learning-adaptive embedding. In particular, we propose a concept of <i>U</i>-minimal sufficient learning-adaptive embeddings, based on which we seek an optimal one to maximize the learning accuracy subject to an embedding constraint. Moreover, when specializing the general framework to classification, we derive a graph embedding classifier based on a hyperlink tensor representing multiple hypergraphs, directed or undirected, characterizing multi-way relations of unstructured data. Numerically, we design algorithms based on blockwise coordinate descent and projected gradient descent to implement linear and feed-forward neural network classifiers, respectively. Theoretically, we establish a learning theory to quantify the generalization error of the proposed method. Moreover, we show, in linear regression, that the one-hot encoder is more preferable among two-stage methods, yet its dimension restriction hinders its predictive performance. For a graph embedding classifier, the generalization error matches up to the standard fast rate or the parametric rate for linear or nonlinear classification. Finally, we demonstrate the utility of the classifiers on two benchmarks in grammatical classification and sentiment analysis.
DOI: 10.6084/m9.figshare.13056140
title: Marginalized Frailty-Based Illness-Death Model: Application to the UK-Biobank Survival Data

description: The UK Biobank is a large-scale health resource comprising genetic, environmental, and medical information on approximately 500,000 volunteer participants in the United Kingdom, recruited at ages 40–69 during the years 2006–2010. The project monitors the health and well-being of its participants. This work demonstrates how these data can be used to yield the building blocks for an interpretable risk-prediction model, in a semiparametric fashion, based on known genetic and environmental risk factors of various chronic diseases, such as colorectal cancer. An illness-death model is adopted, which inherently is a semi-competing risks model, since death can censor the disease, but not vice versa. Using a shared-frailty approach to account for the dependence between time to disease diagnosis and time to death, we provide a new illness-death model that assumes Cox models for the marginal hazard functions. The recruitment procedure used in this study introduces delayed entry to the data. An additional challenge arising from the recruitment procedure is that information coming from both prevalent and incident cases must be aggregated. Lastly, we do not observe any deaths prior to the minimal recruitment age, 40. In this work, we provide an estimation procedure for our new illness-death model that overcomes all the above challenges. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.