cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25573/data.10042655.v2
title: Parque Metropolitano Crane, 25m_Solar Radiation, Pyranometer

description: Parque Natural Metropolitano (PNM) crane station. <br>Location: 8.994410°, - 79.543000°<br>Solar Radiation, Pyranometer, Interval max/min/avg<br><br>Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall. <br><br>The original station was located on the side of the tower at approximately 25m. New sensors were added to the top of the tower in Mar. 2012. The 25m sensor was discontinued on April 2008.<br>
DOI: 10.25573/data.10042661
title: Parque Metropolitano Crane, 25m_Relative Humidity

description: Parque Natural Metropolitano (PNM) crane station.<br> Location: 8.994410°, - 79.543000° <br> Relative Humidity, Interval max/min/avg<br> Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall.<br> This sensor was discontinued when the crane was dismantled in 2021 <br> The original station was located on the side of the tower at approximately 25m. Some sensors were later moved to the top of the tower.<br>
DOI: 10.25573/data.10042661.v6
title: Parque Metropolitano Crane, 25m_Relative Humidity

description: Parque Natural Metropolitano (PNM) crane station.<br> Location: 8.994410°, - 79.543000° <br> Relative Humidity, Interval max/min/avg<br> Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall.<br> This sensor was discontinued when the crane was dismantled in 2021 <br> The original station was located on the side of the tower at approximately 25m. Some sensors were later moved to the top of the tower.<br>
DOI: 10.25573/data.10042661.v5
title: Parque Metropolitano Crane, 25m_Relative Humidity

description: Parque Natural Metropolitano (PNM) crane station. <br>Location: 8.994410°, - 79.543000°<br>Relative Humidity, Interval max/min/avg<br><br>Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall. <br><br>This sensor was discontinued when the crane was dismantled in 2021<br>The original station was located on the side of the tower at approximately 25m. Some sensors were later moved to the top of the tower.<br>
DOI: 10.25573/data.10042673.v10
title: Parque Metropolitano Crane, Top_Solar Radiation, Pyranometer

description: Parque Natural Metropolitano (PNM) crane station. <br>Two sensors: Front and Back<br>Location: 8.994410°, - 79.543000°<br>Solar Radiation, Pyranometer, Interval max/min/avg<br><br>Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall. <br><br>The original station was located on the side of the tower at approximately 20m. Some sensors were later moved to the top of the tower.<br>In 2021 the original crane was dismantled and replaced with a new, 45m crane in Jan. 2022. The temperature and humidity were moved to the top of the crane. An anemometer was mounted near just below the crane boom.
DOI: 10.5517/ccdc.csd.cc2703ky
title: CCDC 2055689: Experimental Crystal Structure Determination

description: Related Article: Kazuya Miyagawa, Ichiro Hisaki, Norihito Fukui, Hiroshi Shinokubo|2021|Chemical Science|12|5224|doi:10.1039/D1SC00438G
DOI: 10.6084/m9.figshare.19795357
title: Comparison of bpMRI and mpMRI PIRADS 3-4 Scores in Predicting Oncologic Outcomes After Radical Prostatectomy

description: ............
DOI: 10.6084/m9.figshare.19795357.v1
title: Comparison of bpMRI and mpMRI PIRADS 3-4 Scores in Predicting Oncologic Outcomes After Radical Prostatectomy

description: ............
DOI: 10.5066/p9b1o4th
title: Gravity and physical property data in the Chico and Willows 1:100,000-scale quadrangles, California

description: The datasets consist of principal fact information of gravity data and density and magnetic properties of hand samples in the Chico and Willows 1:100,000-scale quadrangles, California.
DOI: 10.5203/2yxc-cq08
title: KML Layer - USGS Subbasin HUC 8
DOI: 10.5203/m2s4-nf84
title: Netley-Libau Marsh 1979-2013. Changes in vegetation class distribution between years
DOI: 10.5203/fe2g-0q02
title: September 22, 2014
DOI: 10.25573/data.10042652
title: Parque Metropolitano Crane, 25m_Air Temperature

description: Parque Natural Metropolitano (PNM) crane station.<br> Location: 8.994410°, - 79.543000° <br> Air Temperature, Interval max/min/avg<br> Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall.<br> This sensor was discontinued when the crane was dismantled in 2021<br>
DOI: 10.25573/data.10042652.v6
title: Parque Metropolitano Crane, 25m_Air Temperature

description: Parque Natural Metropolitano (PNM) crane station.<br> Location: 8.994410°, - 79.543000° <br> Air Temperature, Interval max/min/avg<br> Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall.<br> This sensor was discontinued when the crane was dismantled in 2021<br>
DOI: 10.25573/data.10042652.v5
title: Parque Metropolitano Crane, 25m_Air Temperature

description: Parque Natural Metropolitano (PNM) crane station. <br>Location: 8.994410°, - 79.543000°<br>Air Temperature, Interval max/min/avg<br><br>Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall. <br><br>This sensor was discontinued when the crane was dismantled in 2021<br>
DOI: 10.25573/data.10042676.v11
title: Parque Metropolitano Crane_Precipitation

description: Parque Natural Metropolitano (PNM) crane station. <br>Location: 8.994410°, - 79.543000°<br>Precipitation, Interval total precipitation<br><br>Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane is 42 meters tall. <br><br>The original station was located on the side of the tower at approximately 25m. The gauge was moved from the 25m station to the top of the tower in 2009<br><br>In 2021 the original crane was dismantled and replaced with a new, 45m crane locate approximately 20m to the east in Jan. 2022. <br><br>In 2021 the original crane was dismantled and replaced with a new, 45m crane in Jan. 2022. <br>
DOI: 10.5203/gpta-d759
title: LWF Citizen Science Phosphorous Sampling
DOI: 10.5203/myjb-r444
title: Phosphorus_profiles_Feb2018.csv
DOI: 10.5203/d00a-zx52
title: Zipped high resolution geotiff files
DOI: 10.5203/b7nr-xe84
title: Phosphorus_ammonia.csv
DOI: 10.5203/4hhj-c255
title: Manitoba Agriculture Drains Data
DOI: 10.6084/m9.figshare.19210326
title: Electron-Beam Irradiated Recombinant Human Collagen-Phosphorylcholine Corneal Implants Retain Pro-Regeneration Capacity: Raw Data

description: Dataset for Simpson FC, et al. (2022) Electron-Beam Irradiated Recombinant Human Collagen-Phosphorylcholine Corneal Implants Retain Pro-Regeneration Capacity. <em>Front. Bioeng. Biotechnol.</em> 10:883977. doi:10.3389/fbioe.2022.883977
DOI: 10.6084/m9.figshare.19210326.v1
title: Electron-Beam Irradiated Recombinant Human Collagen-Phosphorylcholine Corneal Implants Retain Pro-Regeneration Capacity: Raw Data

description: Dataset for Simpson FC, et al. (2022) Electron-Beam Irradiated Recombinant Human Collagen-Phosphorylcholine Corneal Implants Retain Pro-Regeneration Capacity. <em>Front. Bioeng. Biotechnol.</em> 10:883977. doi:10.3389/fbioe.2022.883977
DOI: 10.5066/p9xqoezv
title: Current-use pesticides, nutrients, chloride, and metals in ponds and stormwater basins of the New Jersey Pinelands, 2018-20

description: The data presented in this data release include concentrations of current-use pesticides, nutrients, chloride, and metals in surface water collected from 60 wetlands including natural ponds, excavated ponds (Xponds), and stormwater basins in the New Jersey Pinelands, 2018-20. Site information, basic water-quality parameters, and method information also are included.
DOI: 10.25345/c5bg2hf26
title: MassIVE MSV000089492 - Proteome profiling of VCaP tumors with KD and re-expression rescue of UGT2B28
title: Proteome profiling of VCaP tumors with KD and re-expression rescue of UGT2B28
DOI: 10.5203/kdve-8825
title: Manitoba Agriculture Drains, Catch Data
DOI: 10.5203/b4x1-6e05
title: September 23, 2014
DOI: 10.5203/3q7j-9762
title: Sampling sites from Seine-Rat River Conservation Districts
DOI: 10.5203/b81f-zd61
title: Lake Manitoba Images
DOI: 10.6084/m9.figshare.19651431
title: Multiple Domain and Multiple Kernel Outcome-Weighted Learning for Estimating Individualized Treatment Regimes

description: Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient’s own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple <i>data domains</i> (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this article, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651431.v2
title: Multiple Domain and Multiple Kernel Outcome-Weighted Learning for Estimating Individualized Treatment Regimes

description: Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient’s own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple <i>data domains</i> (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this article, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651431.v1
title: Multiple domain and multiple kernel outcome-weighted learning for estimating individualized treatment regimes

description: Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient’s own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple <i>data domains</i> (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.19651434
title: Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation

description: This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model’s adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651434.v2
title: Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation

description: This article develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model’s adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.19651434.v1
title: Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation*

description: This paper develops a sparsity-inducing version of Bayesian Causal Forests, a recently proposed nonparametric causal regression model that employs Bayesian Additive Regression Trees and is specifically designed to estimate heterogeneous treatment effects using observational data. The sparsity-inducing component we introduce is motivated by empirical studies where not all the available covariates are relevant, leading to different degrees of sparsity underlying the surfaces of interest in the estimation of individual treatment effects. The extended version presented in this work, which we name Shrinkage Bayesian Causal Forest, is equipped with an additional pair of priors allowing the model to adjust the weight of each covariate through the corresponding number of splits in the tree ensemble. These priors improve the model’s adaptability to sparse data generating processes and allow to perform fully Bayesian feature shrinkage in a framework for treatment effects estimation, and thus to uncover the moderating factors driving heterogeneity. In addition, the method allows prior knowledge about the relevant confounding covariates and the relative magnitude of their impact on the outcome to be incorporated in the model. We illustrate the performance of our method in simulated studies, in comparison to Bayesian Causal Forest and other state-of-the-art models, to demonstrate how it scales up with an increasing number of covariates and how it handles strongly confounded scenarios. Finally, we also provide an example of application using real-world data.
DOI: 10.6084/m9.figshare.17072759.v1
title: Code and data to reproduce "Landscape structure and species life history effects on abundance-occupancy relationships"

description: ## Landscape structure and species life history effects on abundance-occupancy relationships<br><br>### Citation<br><br>Code and data to reproduce the analyses from<br><br>&gt; Dallas, TA, LA Holian, and C Ten Caten 2022. Landscape structure and species life history effects on abundance-occupancy relationships. <br><br><br><br><br><br>## Compiling the project<br><br>To compile the R markdown, the following terminal command can be issued from the directory where the files are all located.<br><br><br>```<br>Rscript -e 'install.packages(c("plyr", "dplyr", "igraph"))'<br><br>Rscript -e 'rmarkdown::render("Dallas2022.Rmd", output_format="pdf_document")'<br><br>```<br><br><br>
DOI: 10.5066/p9jrp0l6
title: Yuma Ridgway's Rail (Rallus obsoletus yumanensis) Population Surveys, Rail Movement, and Potential Habitat at the Salton Sea of California

description: Data were obtained as part of a project assessing risk to the federal and California listed endangered Yuma Ridgway's rail (Rallus obsoletus yumanensis) populations resulting from selenium contaminated agricultural runoff and to inform habitat restoration and management decisions. Four data sets were produced and used to analyze patterns of Yuma Ridgway's rail (Rallus obsoletus yumanensis, renamed from Rallus longirostris yumanensis [R. longirostris yumanensis retained in taxonomy section until further updated in ITIS system, https://www.itis.gov/submit.html]) occupancy and inter-marsh movements and to estimate rail abundance and regional population size to assess extent of selenium exposure of Yuma Ridgway's rail and support management of rail and their habitats at the Salton Sea. These data include site information for call count stations used to monitor secretive marshbirds, specifically Yuma Ridgway's rail, at and near the Salton Sea and Sonny Bono Salton Sea National Wildlife Refuge (SBSSNWR) in 2016 and complete count information for four species of secretive marshbird [Yuma Ridgway's rail, Virginia rail (Rallus limicola), Sora (Porzana carolina), American bittern (Botaurus lentiginosus)], detected among surveys at those sites. The data for call counts conducted between March 8 and April 17, 2016 for the four species is included in the file named Call Count Data.csv and data for call count stations is included in the file name Call Count Station Data.csv. Also included in this release is location information between April 4 and November 17, 2016 for 15 Yuma Ridgway's rails at the Salton Sea marked with radio-downloaded GPS transmitters (YURIRA Locations.csv). Additional data includes potential Yuma Ridgway's rail habitat classified from concurrently occurring land satellite (USGS Landsat) imagery recorded on March 20, 2016 corresponding with telemetry field studies of and call count surveys for Yuma Ridgway's rail (yurira_hab.tif). For more information about methods relating to these data please refer to the subject USGS Open-file Report (Ricca and others, 2022).