cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13056140.v1
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.
DOI: 10.6084/m9.figshare.13607710
title: A Regression Modeling Approach to Structured Shrinkage Estimation

description: Problems involving the simultaneous estimation of multiple parameters arise in many areas of theoretical and applied statistics. A canonical example is the estimation of a vector of normal means. Frequently, structural information about relationships between the parameters of interest is available. For example, in a gene expression denoising problem, genes with similar functions may have similar expression levels. Despite its importance, structural information has not been well-studied in the simultaneous estimation literature, perhaps in part because it poses challenges to the usual geometric or empirical Bayes shrinkage estimation paradigms. This article proposes that some of these challenges can be resolved by adopting an alternate paradigm, based on regression modeling. This approach can naturally incorporate structural information and also motivates new shrinkage estimation and inference procedures. As an illustration, this regression paradigm is used to develop a class of estimators with asymptotic risk optimality properties that perform well in simulations and in denoising gene expression data from a single cell RNA-sequencing experiment.
DOI: 10.6084/m9.figshare.13607710.v2
title: A Regression Modeling Approach to Structured Shrinkage Estimation

description: Problems involving the simultaneous estimation of multiple parameters arise in many areas of theoretical and applied statistics. A canonical example is the estimation of a vector of normal means. Frequently, structural information about relationships between the parameters of interest is available. For example, in a gene expression denoising problem, genes with similar functions may have similar expression levels. Despite its importance, structural information has not been well-studied in the simultaneous estimation literature, perhaps in part because it poses challenges to the usual geometric or empirical Bayes shrinkage estimation paradigms. This article proposes that some of these challenges can be resolved by adopting an alternate paradigm, based on regression modeling. This approach can naturally incorporate structural information and also motivates new shrinkage estimation and inference procedures. As an illustration, this regression paradigm is used to develop a class of estimators with asymptotic risk optimality properties that perform well in simulations and in denoising gene expression data from a single cell RNA-sequencing experiment.
DOI: 10.6084/m9.figshare.13607710.v1
title: A regression modeling approach to structured shrinkage estimation

description: Problems involving the simultaneous estimation of multiple parameters arise in many areas of theoretical and applied statistics. A canonical example is the estimation of a vector of normal means. Frequently, structural information about relationships between the parameters of interest is available. For example, in a gene expression denoising problem, genes with similar functions may have similar expression levels. Despite its importance, structural information has not been well-studied in the simultaneous estimation literature, perhaps in part because it poses challenges to the usual geometric or empirical Bayes shrinkage estimation paradigms. This paper proposes that some of these challenges can be resolved by adopting an alternate paradigm, based on regression modeling. This approach can naturally incorporate structural information and also motivates new shrinkage estimation and inference procedures. As an illustration, this regression paradigm is used to develop a class of estimators with asymptotic risk optimality properties that perform well in simulations and in denoising gene expression data from a single cell RNA-sequencing experiment.
DOI: 10.6084/m9.figshare.13373994
title: Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

description: An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome clinical data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13373994.v2
title: Multicategory Angle-Based Learning for Estimating Optimal Dynamic Treatment Regimes With Censored Data

description: An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome clinical data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.13373994.v1
title: Multicategory Angle-based Learning for Estimating Optimal Dynamic Treatment Regimes with Censored Data

description: An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this paper, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches which are designed to maximize the expected survival time under a binary treatment framework, the proposed method solves the multicategory treatment problem given multiple stages for censored data. Specifically, the proposed method obtains the optimal DTR via integrating estimations of decision rules at multiple stages into a single multicategory classification algorithm without imposing additional constraints, which is also more computationally efficient and robust. In theory, we establish Fisher consistency and provide the risk bound for the proposed estimator under regularity conditions. Our numerical studies show that the proposed method outperforms competing methods in terms of maximizing the conditional survival probability. We apply the proposed method to two real datasets: Framingham heart study data and acquired immunodeficiency syndrome (AIDS) clinical data.
DOI: 10.6084/m9.figshare.12851421
title: More Efficient Policy Learning via Optimal Retargeting

description: Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level, retargeting may induce little to no bias. We then characterize the optimal reference policy and retargeting weights in both binary-action and multi-action settings. We do this in terms of the asymptotic efficient estimation variance of the new learning objective. We further consider weights that additionally control for potential bias due to retargeting. Extensive empirical results in a simulation study and a case study of personalized job counseling demonstrate that retargeting is a fairly easy way to significantly improve any policy learning procedure applied to observational data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.12851421.v2
title: More Efficient Policy Learning via Optimal Retargeting

description: Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level, retargeting may induce little to no bias. We then characterize the optimal reference policy and retargeting weights in both binary-action and multi-action settings. We do this in terms of the asymptotic efficient estimation variance of the new learning objective. We further consider weights that additionally control for potential bias due to retargeting. Extensive empirical results in a simulation study and a case study of personalized job counseling demonstrate that retargeting is a fairly easy way to significantly improve any policy learning procedure applied to observational data. Supplementary materials for this article are available online.
DOI: 10.25984/1784284
title: U.S. Wind Siting Regulation and Zoning Ordinances

description: A collection of documented wind siting ordinances at the state, county, township, and local level throughout the United States. The data were compiled from several sources including, DOE's Wind Exchange Ordinance Database (https://windexchange.energy.gov/policies-incentives), National Conference of State and Legislatures Wind Energy Siting (https://www.ncsl.org/research/energy/state-wind-energy-siting.aspx), and scholarly legal articles. The citations for each ordinance are included in the spreadsheet.
DOI: 10.6084/m9.figshare.14669952
title: AHP Result.csv

description: AHP result from expert assessment related to solid waste management strategy in university
DOI: 10.6084/m9.figshare.14669958
title: Annex 2 Questionnaire for Risk assessment of waste management factors.pdf

description: Questionnaire of FMEA to assess risk failure
DOI: 10.34740/kaggle/dsv/2267407
title: Twitter: Trending Tweets per week (updated weekly)
DOI: 10.6084/m9.figshare.14669970
title: Ph D Thesis Appendix Rstudio Code

description: The processes determining dissolved organic carbon (DOC) character and concentration in groundwater in different geological environments<br>
DOI: 10.21227/ecjt-ze13
title: In-Band Full-Duplex Underwater Acoustic Communication Measurements: Self-interference

description: The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers. Two types of signals, binary phase-shift keying (BPSK) and orthogonal frequency-division multiplexing (OFDM), were transmitted at the center frequency of 28 kHz. The receptions were recorded in .wav audio files by eighter high-precision digital hydrophones. In addition to the acoustic data, a complete set of source information, environmental measurements, and processed impulse responses are included in the data package. Matlab programs are also provided to retrieve the data and facilitate further analysis.
DOI: 10.6084/m9.figshare.13553267
title: Threats and challenges related to grazing paddocks: Recovery of extremely overgrazed grassland after grazing exclusion

description: Overgrazing causes serious problems for rangeland ecosystems and human livelihoods globally. Sheep grazing paddocks with fixed fences have become increasingly widespread in Europe; however, their effects on biodiversity have rarely been discussed. Our aim was to measure the degradation of grassland vegetation in an experimental sheep paddock in Central-Hungary, and evaluate recovery of alkali grasslands after the exclusion of grazing. We compared the vegetation of heavily overgrazed parcels to parcels excluded from overgrazing, treated with three doses of manure (0, 20 and 40t/ha), in the first three years of grassland recovery. The overgrazed parcels were dominated by nutrient-demanding ruderal plant species and the cover of target grassland species was only 19.8% in the third year. After grazing exclusion, the cover of target grassland species increased to 81.0% for Year 3. The dominant grass <i>Festuca pseudovina</i> became more abundant in the excluded parcels (51.1%) than in the overgrazed ones (8.9%). At the end of the experiment, the rate of degradation (0.2 vs. 3.7) and the cover-weighted ecological indicator values for nutrients (3.8 vs. 5.1) were significantly lower in the exclosures than in the overgrazed parcels. Manuring did not have a significant effect on grassland recovery; thus, it is not necessary in the studied ecosystem. We recommend avoiding grazing paddocks with fixed fence especially in areas harboring habitats of high conservation value. Besides resulting in the degradation of grasslands, grazing paddocks also act as landscape scars and propagule sources of problem species that might negatively affect the surrounding landscape.
DOI: 10.6084/m9.figshare.13553267.v1
title: Threats and challenges related to grazing paddocks: Recovery of extremely overgrazed grassland after grazing exclusion

description: Overgrazing causes serious problems for rangeland ecosystems and human livelihoods globally. Sheep grazing paddocks with fixed fences have become increasingly widespread in Europe; however, their effects on biodiversity have rarely been discussed. Our aim was to measure the degradation of grassland vegetation in an experimental sheep paddock in Central-Hungary, and evaluate recovery of alkali grasslands after the exclusion of grazing. We compared the vegetation of heavily overgrazed parcels to parcels excluded from overgrazing, treated with three doses of manure (0, 20 and 40t/ha), in the first three years of grassland recovery. The overgrazed parcels were dominated by nutrient-demanding ruderal plant species and the cover of target grassland species was only 19.8% in the third year. After grazing exclusion, the cover of target grassland species increased to 81.0% for Year 3. The dominant grass <i>Festuca pseudovina</i> became more abundant in the excluded parcels (51.1%) than in the overgrazed ones (8.9%). At the end of the experiment, the rate of degradation (0.2 vs. 3.7) and the cover-weighted ecological indicator values for nutrients (3.8 vs. 5.1) were significantly lower in the exclosures than in the overgrazed parcels. Manuring did not have a significant effect on grassland recovery; thus, it is not necessary in the studied ecosystem. We recommend avoiding grazing paddocks with fixed fence especially in areas harboring habitats of high conservation value. Besides resulting in the degradation of grasslands, grazing paddocks also act as landscape scars and propagule sources of problem species that might negatively affect the surrounding landscape.
DOI: 10.25919/z7ad-q943
title: Southern HII Region Discovery Survey (SHRDS): Dataset 3

description: The Southern HII Region Discovery Survey is an Australia Telescope Compact Array 4-6 and 8-10 GHz radio continuum and radio recombination line survey of Milky Way HII regions (Wenger et al., 2021). This data archive contains all relevant intermediate data products from the final SHRDS data release (the Full Catalog). For each observed HII region and calibrator field, this archive contains (1) a 4 GHz bandwidth, full-Stokes, multi-scale, multi-frequency synthesis (MS-MFS) continuum FITS image, (2) up to sixteen 256 MHz bandwidth, full-Stokes, MS-MFS continuum FITS images covering the full 4 GHz bandwidth, (3) a Stokes I MS-MFS FITS image of each RRL transition, (4) a Stokes I multi-scale FITS data cube of each RRL transition, and (5) a FITS image containing the watershed segmentation region for each identified HII region at each observed frequency. The archive also includes the relevant residual images/cubes and primary beam images for all of these data products, as well as all of the images and cubes smoothed to a common angular resolution.
DOI: 10.7910/dvn/i7mub8
title: Open materials for "From metaphor to computation: Constructing the potential landscape for multivariate psychological formal models"

description: Please find the description of this dataset at `Readme.md`.
DOI: 10.6084/m9.figshare.13177264.v1
title: Expansion of a size disaggregation profile library for particulate matter emissions processing from three generic profiles to 36 source-type-specific profiles

description: This study describes a significant upgrade to the particulate matter (PM) size disaggregation profile library used for preparing emissions files for the GEM-MACH (Global Environmental Multiscale-Modelling Air-quality and CHemistry) chemical transport model (CTM). This model uses a sectional (bin) approach to represent the PM size distribution, where one configuration employs 12 size bins to disaggregate PM<sub>2.5</sub> and PM<sub>10</sub> inventory emissions into the first 10 bins ranging from 0.01 to 10.24 μm in diameter. For the size disaggregation step, a small library of three generic PM size disaggregation profiles is currently applied for three broad source categories (area, mobile, and point). However, as might be expected, these generic profiles are not always representative: for example, emissions from two very different area sources ‒ paved road dust and residential wood combustion ‒ are disaggregated using the same generic size distribution profile. In order to improve the current small PM size disaggregation profile library, a comprehensive literature review was conducted: over 100 relevant publications were identified and PM size distribution profiles for 36 different emission source types were selected and compiled. These 36 source-type-specific PM size distribution profiles were then combined based on process type with corresponding PM speciation profiles to create a library of chemically speciated and size-resolved PM disaggregation profiles. This library can now be used by the SMOKE (Sparse Matrix Operator Kernel Emissions) emissions processing system for the 12-bin version of GEM-MACH to perform PM chemical speciation and size allocation in one step. The size-profile data collected and compiled in this study may also be used for emissions processing for other CTMs with a size-resolved representation of PM. Details of the compilation of the 36 PM size disaggregation profiles are discussed, and the differences in processed PM emissions based on the current and updated PM size disaggregation profile libraries are shown. <i>Implications</i>: A new and expanded particulate matter (PM) size disaggregation profile library covering 36 emission source types has been developed based on an extensive literature review. Its use can produce significant changes in the size allocation of bulk PM inventory emissions processed for input to size-resolved PM chemical transport models. Such models are used to predict atmospheric visibility and to simulate the interactions of aerosol particles with atmospheric radiation and with clouds. The use of more accurate, size-resolved primary PM emissions by these models should improve their predictive skill for atmospheric PM processes affecting air quality, meteorology, and climate.