cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.3485525
title: One-Step Estimator Paths for Concave Regularization

description: The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ℓ<sub>0</sub> and ℓ<sub>1</sub> norms. However, lasso ℓ<sub>1</sub>-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ℓ<sub>1</sub> penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-ℓ<sub>1</sub> and ℓ<sub>0</sub> penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485525.v2
title: One-Step Estimator Paths for Concave Regularization

description: The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ℓ<sub>0</sub> and ℓ<sub>1</sub> norms. However, lasso ℓ<sub>1</sub>-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ℓ<sub>1</sub> penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-ℓ<sub>1</sub> and ℓ<sub>0</sub> penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485525.v3
title: One-Step Estimator Paths for Concave Regularization

description: The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ℓ<sub>0</sub> and ℓ<sub>1</sub> norms. However, lasso ℓ<sub>1</sub>-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ℓ<sub>1</sub> penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-ℓ<sub>1</sub> and ℓ<sub>0</sub> penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485525.v5
title: One-Step Estimator Paths for Concave Regularization

description: The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ℓ<sub>0</sub> and ℓ<sub>1</sub> norms. However, lasso ℓ<sub>1</sub>-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ℓ<sub>1</sub> penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-ℓ<sub>1</sub> and ℓ<sub>0</sub> penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485525.v4
title: One-Step Estimator Paths for Concave Regularization

description: The statistics literature of the past 15 years has established many favorable properties for sparse diminishing-bias regularization: techniques that can roughly be understood as providing estimation under penalty functions spanning the range of concavity between ℓ<sub>0</sub> and ℓ<sub>1</sub> norms. However, lasso ℓ<sub>1</sub>-regularized estimation remains the standard tool for industrial Big Data applications because of its minimal computational cost and the presence of easy-to-apply rules for penalty selection. In response, this article proposes a simple new algorithm framework that requires no more computation than a lasso path: the path of one-step estimators (POSE) does ℓ<sub>1</sub> penalized regression estimation on a grid of decreasing penalties, but adapts coefficient-specific weights to decrease as a function of the coefficient estimated in the previous path step. This provides sparse diminishing-bias regularization at no extra cost over the fastest lasso algorithms. Moreover, our gamma lasso implementation of POSE is accompanied by a reliable heuristic for the fit degrees of freedom, so that standard information criteria can be applied in penalty selection. We also provide novel results on the distance between weighted-ℓ<sub>1</sub> and ℓ<sub>0</sub> penalized predictors; this allows us to build intuition about POSE and other diminishing-bias regularization schemes. The methods and results are illustrated in extensive simulations and in application of logistic regression to evaluating the performance of hockey players. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485522
title: Path Boxplots: A Method for Characterizing Uncertainty in Path Ensembles on a Graph

description: Graphs are powerful and versatile data structures that can be used to represent a wide range of different types of information. In this article, we introduce a method to analyze and then visualize an important class of data described over a graph—namely, ensembles of paths. Analysis of such path ensembles is useful in a variety of applications, in diverse fields such as transportation, computer networks, and molecular dynamics. The proposed method generalizes the concept of <i>band depth</i> to an ensemble of paths on a graph, which provides a center-outward ordering on the paths. This ordering is, in turn, used to construct a generalization of the conventional boxplot or whisker plot, called a <i>path boxplot</i>, which applies to paths on a graph. The utility of path boxplot is demonstrated for several examples of path ensembles including paths defined over computer networks and roads. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.3485522.v1
title: Path Boxplots: A Method for Characterizing Uncertainty in Path Ensembles on a Graph

description: Graphs are powerful and versatile data structures that can be used to represent a wide range of different types of information. In this paper, we introduce a method to analyze and then visualize an important class of data described over a graph—namely, ensembles of paths. Analysis of such path ensembles is useful in a variety of applications, in diverse fields such as transportation, computer networks, and molecular dynamics. The proposed method generalizes the concept of <i>band depth</i> to an ensemble of paths on a graph, which provides an center-outward ordering on the paths. This ordering is, in turn, used to construct a generalization of the conventional boxplot or whisker plot, called a <i>path boxplot</i>, which applies to paths on a graph. The utility of path boxplot is demonstrated for several examples of path ensembles including paths defined over computer networks and roads.
DOI: 10.6084/m9.figshare.3485273
title: Journal Adherence to Reporting Guidelines

description: We used a google form to create this table of journal adherence to reporting guidelines in hematology.
DOI: 10.6084/m9.figshare.3485273.v1
title: Journal Adherence to Reporting Guidelines

description: We used a google form to create this table of journal adherence to reporting guidelines in hematology.
DOI: 10.6084/m9.figshare.3485273.v3
title: Journal Adherence to Reporting Guidelines

description: We used a google form to create this table of journal adherence to reporting guidelines in hematology.
DOI: 10.6084/m9.figshare.3485273.v2
title: Journal Adherence to Reporting Guidelines

description: We used a google form to create this table of journal adherence to reporting guidelines in hematology.
DOI: 10.6084/m9.figshare.3485267
title: Tests on Thin Reinforced Concrete Walls Subjected to In-plane and Out-of-plane Cyclic Loading - TW4 (2/4)

description: The present dataset describes an experimental campaign on five thin T-shaped reinforced concrete walls. The tests aimed at assessing the influence of wall thickness on member stability, the role of lap splices on damage distribution and displacement ductility, and the effects of the simultaneous application of out-of-plane loading on the member response.Further data can be downloaded from the links below.<br> If you use the data, please cite: Almeida, J. P., O. Prodan, A. Rosso, and K. Beyer. 2016. Tests on Thin Reinforced Concrete Walls Subjected to in-Plane and out-of-Plane Cyclic Loading. <i>Earthquake Spectra</i>.
DOI: 10.6084/m9.figshare.3485237
title: sageAbundance

description: This fileset contains data and R code necessary to reproduce the results from Tredennick et al. 2016 to appear in <i>Ecosphere</i>. For more details, see the README file or visit the GitHub page for this project (https://github.com/atredennick/sageAbundance).
DOI: 10.6084/m9.figshare.3485231
title: Alouatta Sanctuary Review Audio

description: This is the audio connected to the post about Alouatta Sanctuary.
DOI: 10.6084/m9.figshare.3485231.v1
title: Alouatta Sanctuary Review Audio

description: This is the audio connected to the post about Alouatta Sanctuary.
DOI: 10.6084/m9.figshare.3485216
title: Raw data for Di Battista et al - "Altered blood biomarker profiles in athletes with a history of repetitive head impacts". 2016.xls

description: This is a raw dataset for a published manuscript in the journal PLOS One
DOI: 10.6084/m9.figshare.3485204
title: Arlington, Massachusetts - Aerial imagery object identification dataset for building and road detection, and building height estimation

description: This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413. Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).
DOI: 10.6084/m9.figshare.3485201
title: Metadata file from the manuscript 'Alterations in airway microbiota in patients with acute lung injury after burn and inhalation injury'

description: Metadata file for 16S rRNA gene sequencing done on airway samples from victims of burn and inhalation injury.Duplicate barcoded paired-end reads were sequenced on the Illumina MiSeq using the molecule tagging method described by Lundberg et. al. (Nature Methods, 2013, DOI: 10.1038/nmeth.2634) and the OTU table was generated using the MTToolbox pipeline (Yourstone, 2015, BMC Bioinformatics, DOI: 10.1168/1471-2105-15-284). The OTU table was imported into the program Explicet (www.explicet.org; Robertson et. al., Bioinformatics, 2013, DOI: 10.1093/bioinformatics/btt526), OTUs identified as the same species were condensed, and this table was exported and used for analysis in R.
DOI: 10.6084/m9.figshare.3485201.v1
title: Metadata

description: Metadata file for 16S rRNA gene sequencing done on airway samples from victims of burn and inhalation injury. Barcodes for paired-end reads on the Illumina MiSeq included. These samples were sequenced using the molecule method described by Lundberg et. al. (Nature Methods, 2013, DOI: 10.1038/nmeth.2634) and the OTU table was generated using the MTToolbox pipeline (Yourstone, 2015, BMC Bioinformatics, DOI: 10.1168/1471-2105-15-284).
DOI: 10.6084/m9.figshare.3485201.v2
title: Metdata file for analyzing alterations in airway microbiota in patients with acute lung injury after burn and inhalation injury.

description: Metadata file for 16S rRNA gene sequencing done on airway samples from victims of burn and inhalation injury. Barcodes for paired-end reads on the Illumina MiSeq included. These samples were sequenced using the molecule method described by Lundberg et. al. (Nature Methods, 2013, DOI: 10.1038/nmeth.2634) and the OTU table was generated using the MTToolbox pipeline (Yourstone, 2015, BMC Bioinformatics, DOI: 10.1168/1471-2105-15-284).
DOI: 10.6084/m9.figshare.3485201.v3
title: Alterations in airway microbiota in patients with acute lung injury after burn and inhalation injury metadata file.

description: Metadata file for 16S rRNA gene sequencing done on airway samples from victims of burn and inhalation injury. Barcodes for paired-end reads on the Illumina MiSeq included. These samples were sequenced using the molecule method described by Lundberg et. al. (Nature Methods, 2013, DOI: 10.1038/nmeth.2634) and the OTU table was generated using the MTToolbox pipeline (Yourstone, 2015, BMC Bioinformatics, DOI: 10.1168/1471-2105-15-284).
DOI: 10.6084/m9.figshare.3485144
title: Warming and drought combine to increase pest fitness on urban trees

description: Urban habitats are characterized by impervious surfaces, which increase temperatures and reduce water availability to plants. Three primary hypotheses have been proposed to explain why multiple herbivorous arthropods are more abundant and damaging in cities, and support has been found for each. First, less complex vegetation may reduce biological control of pests. Second, plant stress can increase plant quality for pests. And third, urban warming can directly increase pest fitness and abundance. We test the hypothesis that urban warming and drought stress combine to increase the fitness and abundance of the scale insect, <i>Melanaspis tenebricosa</i>, a pest of urban trees the southeastern U.S. We did this by manipulating drought stress across an existing mosaic of urban warming. We found support for the additive effect of temperature and drought stress such that female embryo production and body size increased with temperature and was greater on drought-stressed than watered trees. This study provides further evidence that drivers of pest insect outbreaks act in concert, rather than independently, and calls for more research that manipulates multiple abiotic factors related to urbanization and climate change to predict their effects on ecological interactions. <br>
DOI: 10.6084/m9.figshare.3485144.v1
title: Warming and drought combine to increase pest fitness on urban trees

description: Urban habitats are characterized by impervious surfaces, which increase temperatures and reduce water availability to plants. Three primary hypotheses have been proposed to explain why multiple herbivorous arthropods are more abundant and damaging in cities, and support has been found for each. First, less complex vegetation may reduce biological control of pests. Second, plant stress can increase plant quality for pests. And third, urban warming can directly increase pest fitness and abundance. We test the hypothesis that urban warming and drought stress combine to increase the fitness and abundance of the scale insect, <i>Melanaspis tenebricosa</i>, a pest of urban trees the southeastern U.S. We did this by manipulating drought stress across an existing mosaic of urban warming. We found support for the additive effect of temperature and drought stress such that female embryo production and body size increased with temperature and was greater on drought-stressed than watered trees. This study provides further evidence that drivers of pest insect outbreaks act in concert, rather than independently, and calls for more research that manipulates multiple abiotic factors related to urbanization and climate change to predict their effects on ecological interactions. <br>
DOI: 10.6084/m9.figshare.3485135
title: Clinical characteristics and dose-volume histogram parameters associated with the development of pleural effusions in non-small cell lung cancer patients treated with chemoradiation therapy

description: <b>Background:</b> To investigate descriptive characteristics and dose metric (DM) parameters associated with development of pleural effusions (PlEf) in non-small cell lung cancer (NSCLC) treated with definitive chemoradiation therapy (CRT). <b>Materials and methods:</b> We retrospectively assessed treatment records and follow-up imaging of 66 NSCLC patients to identify PlEf formation after CRT. PlEf association between mean heart dose (MHD), mean lung dose (MLD), heart V<sub>5</sub>–V<sub>60</sub> (HV), and lung V<sub>5</sub>–V<sub>60</sub> (LV) were evaluated using Cox Proportional Hazard Models. <b>Results:</b> A total of 52% (34 of 66 patients) of our population developed PlEf and the actuarial rates at 6 months, 12 months, and 18 months were 7%, 30%, and 42%, respectively. Median time to diagnosis was five months (range 0.06–27 months). The majority of PlEfs were grade one (67%) and developed at a median of four (0.06–13) months, followed by grade two (15%) at a median 11 (5–12) months, and grade three (18%) at a median of 11 (3–27) months. On multivariate analysis, increasing HV<sub>5</sub>–HV<sub>50</sub>, LV<sub>5</sub>–LV<sub>50</sub>, MHD, and MLD were associated with greater risk of PlEf. Higher grade PlEf was also associated with higher doses of radiation to the heart, while lung DM parameters were not significantly associated with higher PlEf grades. At five-months post-CRT, MHD of 25 Gy was associated with a 100% chance of grade one PlEf, an 82% risk of grade two PlEf, and a 19% risk of grade three PlEf. <b>Conclusions:</b> Post-CRT PlEf is common in NSCLC with the majority being grade one. Increasing heart and lung irradiation was associated with increased risk of PlEf. Increasing heart irradiation also correlated with development of increasing grades of PlEf. The impact of potential cardiopulmonary toxicity and resultant PlEfs after CRT requires additional study.
DOI: 10.6084/m9.figshare.3485135.v1
title: Clinical characteristics and dose-volume histogram parameters associated with the development of pleural effusions in non-small cell lung cancer patients treated with chemoradiation therapy

description: <b>Background:</b> To investigate descriptive characteristics and dose metric (DM) parameters associated with development of pleural effusions (PlEf) in non-small cell lung cancer (NSCLC) treated with definitive chemoradiation therapy (CRT). <b>Materials and methods:</b> We retrospectively assessed treatment records and follow-up imaging of 66 NSCLC patients to identify PlEf formation after CRT. PlEf association between mean heart dose (MHD), mean lung dose (MLD), heart V<sub>5</sub>–V<sub>60</sub> (HV), and lung V<sub>5</sub>–V<sub>60</sub> (LV) were evaluated using Cox Proportional Hazard Models. <b>Results:</b> A total of 52% (34 of 66 patients) of our population developed PlEf and the actuarial rates at 6 months, 12 months, and 18 months were 7%, 30%, and 42%, respectively. Median time to diagnosis was five months (range 0.06–27 months). The majority of PlEfs were grade one (67%) and developed at a median of four (0.06–13) months, followed by grade two (15%) at a median 11 (5–12) months, and grade three (18%) at a median of 11 (3–27) months. On multivariate analysis, increasing HV<sub>5</sub>–HV<sub>50</sub>, LV<sub>5</sub>–LV<sub>50</sub>, MHD, and MLD were associated with greater risk of PlEf. Higher grade PlEf was also associated with higher doses of radiation to the heart, while lung DM parameters were not significantly associated with higher PlEf grades. At five-months post-CRT, MHD of 25 Gy was associated with a 100% chance of grade one PlEf, an 82% risk of grade two PlEf, and a 19% risk of grade three PlEf. <b>Conclusions:</b> Post-CRT PlEf is common in NSCLC with the majority being grade one. Increasing heart and lung irradiation was associated with increased risk of PlEf. Increasing heart irradiation also correlated with development of increasing grades of PlEf. The impact of potential cardiopulmonary toxicity and resultant PlEfs after CRT requires additional study.