cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.20090004.v1
title: High-dimensional Multi-Task Learning using Multivariate Regression and Generalized Fiducial Inference

description: Over the past decades, the Multi-Task Learning (MTL) problem has attracted much attention in the artificial intelligence and machine learning communities. However, most published work in this area focuses on point estimation; i.e., estimating model parameters and/or making predictions. This paper studies another important aspect of the MTL problem: uncertainty quantification for model choices and predictions. To be more specific, this paper approaches the MTL problem with multivariate regression and develops a novel method for deriving a probability density function on the space of all potential regression models. With this density function, point estimates, as well as confidence and prediction ellipsoids, can be obtained for quantities of interest, such as future observations. The proposed method, termed GMTask, is based on the generalized fiducial inference (GFI) framework and is shown to enjoy desirable theoretical properties. Its promising empirical properties are illustrated via a sequence of numerical experiments and applications to two real data sets.
DOI: 10.6084/m9.figshare.20039705
title: Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

description: Alzheimer’s disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of <i>β</i> amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer’s disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer’s Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. 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.20039705.v2
title: Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

description: Alzheimer’s disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of <i>β</i> amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer’s disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer’s Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.5281/zenodo.6862503
title: Haptic Reality - Evaluation data

description: Raw data collected in the evaluation of the new interaction technique for force feedback desktop haptic devices called Haptic Reality.
DOI: 10.6084/m9.figshare.20039705.v1
title: Mapping the Genetic-Imaging-Clinical Pathway with Applications to Alzheimer’s Disease

description: Alzheimer’s disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of <i>β</i> amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer’s Disease (AD). The aim of this paper is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer’s Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
DOI: 10.5281/zenodo.6862502
title: Haptic Reality - Evaluation data

description: Raw data collected in the evaluation of the new interaction technique for force feedback desktop haptic devices called Haptic Reality.
DOI: 10.6084/m9.figshare.20340849
title: Scapula shape data - Character states

description: TPS file shape data for lizard scapulas with included character states and nexus file tree.
DOI: 10.6084/m9.figshare.20340849.v1
title: Scapula shape data - Character states

description: TPS file shape data for lizard scapulas with included character states and nexus file tree.
DOI: 10.23645/epacomptox.16744162.v3
title: The US CPSC/EPA Flame Retardant and Organohalogen Flame Retardant Inventory

description: Katherine Phillips dataset for journal article "The US CPSC/EPA Flame Retardant and Organohalogen Flame Retardant Inventory" for Scientific Data<br>Science Inventory, CCTE products: https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25
DOI: 10.6084/m9.figshare.20340876
title: Readme.txt

description: Read me file for: "Acoustic and postural displays in a miniature and transparent teleost fish, <em>Danionella dracula</em>"
DOI: 10.6084/m9.figshare.20340876.v1
title: Readme.txt

description: Read me file for: "Acoustic and postural displays in a miniature and transparent teleost fish, <em>Danionella dracula</em>"
DOI: 10.5281/zenodo.6862888
title: Deroptyus accipitrinus zanfona 1

description: .
DOI: 10.5281/zenodo.6862887
title: Deroptyus accipitrinus zanfona 1

description: .
DOI: 10.6084/m9.figshare.20340882
title: Execution speeds of ART machine in solving N equations

description: Execution speeds of ART machine in solving N equations
DOI: 10.6084/m9.figshare.20340882.v1
title: Execution speeds of ART machine in solving N equations

description: Execution speeds of ART machine in solving N equations
DOI: 10.6084/m9.figshare.20073587.v2
title: Set-Valued Support Vector Machine with Bounded Error Rates

description: This article concerns cautious classification models that are allowed to predict a set of class labels or reject to make a prediction when the uncertainty in the prediction is high. This set-valued classification approach is equivalent to the task of acceptance region learning, which aims to identify subsets of the input space, each of which guarantees to cover observations in a class with at least a predetermined probability. We propose to directly learn the acceptance regions through risk minimization, by making use of a truncated hinge loss and a constrained optimization framework. Collectively our theoretical analyses show that these acceptance regions, with high probability, satisfy simultaneously two properties: (a) they guarantee to cover each class with a noncoverage rate bounded from above; (b) they give the least ambiguous predictions among all the acceptance regions satisfying (a). An efficient algorithm is developed and numerical studies are conducted using both simulated and real data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20073587.v1
title: Set-Valued Support Vector Machine with Bounded Error Rates

description: This article concerns cautious classification models that are allowed to predict a set of class labels or reject to make a prediction when the uncertainty in the prediction is high. This set-valued classification approach is equivalent to the task of acceptance region learning, which aims to identify subsets of the input space, each of which guarantees to cover observations in a class with at least a predetermined probability. We propose to directly learn the acceptance regions through risk minimization, by making use of a truncated hinge loss and a constrained optimization framework. Collectively our theoretical analyses show that these acceptance regions, with high probability, satisfy simultaneously two properties: (1) they guarantee to cover each class with a non-coverage rate bounded from above; (2) they give the least ambiguous predictions among all the acceptance regions satisfying (1). An efficient algorithm is developed and numerical studies are conducted using both simulated and real data.
DOI: 10.6084/m9.figshare.20073587
title: Set-Valued Support Vector Machine with Bounded Error Rates

description: This article concerns cautious classification models that are allowed to predict a set of class labels or reject to make a prediction when the uncertainty in the prediction is high. This set-valued classification approach is equivalent to the task of acceptance region learning, which aims to identify subsets of the input space, each of which guarantees to cover observations in a class with at least a predetermined probability. We propose to directly learn the acceptance regions through risk minimization, by making use of a truncated hinge loss and a constrained optimization framework. Collectively our theoretical analyses show that these acceptance regions, with high probability, satisfy simultaneously two properties: (a) they guarantee to cover each class with a noncoverage rate bounded from above; (b) they give the least ambiguous predictions among all the acceptance regions satisfying (a). An efficient algorithm is developed and numerical studies are conducted using both simulated and real data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20340894
title: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology

description: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology
DOI: 10.6084/m9.figshare.20340894.v2
title: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology

description: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology
DOI: 10.6084/m9.figshare.20340894.v1
title: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology

description: Execution time in Microseconds for analytically solving, producing close loop solutions for a System of N (up to 60 unknown variables) Linear Equations using LU Decomposition and partial pivoting methodology
DOI: 10.6084/m9.figshare.16989811
title: Data for L'Allier et al. vervet dispersal paper

description: Dispersal Mode: Summary of vervet male transfers between groups at Lake Nabugabo, Uganda, with type and mode of dispersal for 2012-2019.Seasonality: Timing of dispersals and conceptionsTraj_Elo: Difference between power trajectories (slope) of dispersing males before and/or after transferring to the slope (mean+SD) of the group. Sr&RankChange: Sex ratio and dominance rank before (resident group) and after (target group) dispersal for males for whom we have sufficient pre-and post-dispersal data.Hormones: Mean male androgen and glucocorticoid levels at four different times points relative to a given dispersal event (day 0); Baseline before = 61-240 days before dispersal, Dispersal before = 1-60 days before dispersal, Dispersal after = 1-60 days after dispersal, and Baseline after = 61-240 days after dispersal; NAN=not a number.<br><br>
DOI: 10.6084/m9.figshare.16989811.v1
title: Data for L'Allier et al. vervet dispersal paper

description: Dispersal Mode: Summary of vervet male transfers between groups at Lake Nabugabo, Uganda, with type and mode of dispersal for 2012-2019.Seasonality: Timing of dispersals and conceptionsTraj_Elo: Difference between power trajectories (slope) of dispersing males before and/or after transferring to the slope (mean+SD) of the group. Sr&RankChange: Sex ratio and dominance rank before (resident group) and after (target group) dispersal for males for whom we have sufficient pre-and post-dispersal data.Hormones: Mean male androgen and glucocorticoid levels at four different times points relative to a given dispersal event (day 0); Baseline before = 61-240 days before dispersal, Dispersal before = 1-60 days before dispersal, Dispersal after = 1-60 days after dispersal, and Baseline after = 61-240 days after dispersal; NAN=not a number.<br><br>
DOI: 10.34740/kaggle/dsv/3957886
title: ics_microclimate
DOI: 10.6084/m9.figshare.20340978
title: Jamovi datasets used in Analysis

description: Jamovi datasets used in Analysis
DOI: 10.5281/zenodo.6862959
title: Single-Aspect Phenotypes for Modality, Perspective, and Time Collected Via a High-Throughput Imaging System

description: SAPP and SAPM are dataset repositories used to compute Single-Aspect Phenotypes. SAPP has 28 datasets and SAPM has 12 datasets. Each dataset contains: Two species physically different. Buckwheat is a thin plant with a variety sizes of leaves and Sunflower is a bushy plant that contains flowering; the most commonly used induced environments in plant phenotyping such as a control and drought-induced; a temporal resolution that begins with the plants vegetative stage and ends with the plant fully matured; modalities (infrared, visible, near infrared) that are commonly used in plant phenotyping analysis; and multiple perspectives that are becoming widely acquired in plant phenotyping analysis due to its potential for three dimensional analysis. We thank Vincent Stoeger for acquiring the dataset using LemnaTec at the University of Nebraska-Lincoln. If you use this dataset, please cite this paper:
description: This material is based upon work supported by the National Science Foundation under Grant No. DGE-1735362. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Also, the authors acknowledge the support provided by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production.
DOI: 10.5281/zenodo.6862985
title: Intention to utilize telehealth service in Bangladesh

description: This data consists of information on participants' knowledge, perceived benefit, perceived concerns, and predispositions related to telehealth services in Bangladesh. In the data set, k1 to k5 indicted the items of knowledge. Similarly, pb1 to pb4 indicate perceived benefit, pc indicates the item of perceived concern, and pd1 to pd2 indicates the items of predisposition. This data set also includes information related to the demographic and perceived health status information.
DOI: 10.5281/zenodo.6819750
title: Regional-Scale Lithospheric Recycling on Venus via Peel-Back Delamination

description: This archive contains input parameter files and a selection of output files for each of the models presented in the manuscript titled 'Regional-Scale Lithospheric Recycling on Venus via Peel-Back Delamination'. Models were run using the code, StagYY (Tackley, 2008), and visualization and post-processing was done using StagLab (Crameri, 2018). Output files are given for two times for each model: the first as the slab encounters the 710 km phase transitions and the second near the time of slab break-off. Output files containing velocity, pressure, temperature, and viscosity data are provided.
DOI: 10.5517/ccdc.csd.cc27tjl6
title: CCDC 2080118: Experimental Crystal Structure Determination

description: Related Article: Wenwen Yi, Xiao-Yuan Lian, Zhizhen Zhang|2022|Phytochemistry|201|113292|doi:10.1016/j.phytochem.2022.113292
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc28bvj0
title: CCDC 2095802: Experimental Crystal Structure Determination

description: Related Article: Nilamuni H. de Silva, Anthony Dahdah, Ewan W. Blanch, Helmut M. Hügel, Subashani Maniam|2022|Eur.J.Med.Chem.|240|114566|doi:10.1016/j.ejmech.2022.114566
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2993nd
title: CCDC 2123923: Experimental Crystal Structure Determination

description: Related Article: Jian-Hua Qin, Zhi Xiao, Ji-Rui Zhang, Shu-Xian Ren, Yu-Xin Hu, Xiao-Gang Yang, Lu-Fang Ma, Dong-Sheng Li|2022|Dyes Pigm.|205|110506|doi:10.1016/j.dyepig.2022.110506
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b51bx
title: CCDC 2149798: Experimental Crystal Structure Determination

description: Related Article: Hojatollah Fatemikia, Hassan Keypour, Hamid Zeynali, Roya Karamian, Nika Ranjbar, Robert William Gable|2022|J.Inorg.Biochem.|235|111910|doi:10.1016/j.jinorgbio.2022.111910
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b95sm
title: CCDC 2153780: Experimental Crystal Structure Determination

description: Related Article: Miaofeng Ren, Xiaoyang Yan, Xiaojing Lai, Jin-Biao Liu, Hongwei Zhou, Guanyinsheng Qiu|2022|Mol.Catal.|528|112413|doi:10.1016/j.mcat.2022.112413
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b95tn
title: CCDC 2153781: Experimental Crystal Structure Determination

description: Related Article: Miaofeng Ren, Xiaoyang Yan, Xiaojing Lai, Jin-Biao Liu, Hongwei Zhou, Guanyinsheng Qiu|2022|Mol.Catal.|528|112413|doi:10.1016/j.mcat.2022.112413
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b95vp
title: CCDC 2153782: Experimental Crystal Structure Determination

description: Related Article: Miaofeng Ren, Xiaoyang Yan, Xiaojing Lai, Jin-Biao Liu, Hongwei Zhou, Guanyinsheng Qiu|2022|Mol.Catal.|528|112413|doi:10.1016/j.mcat.2022.112413
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2b9cxy
title: CCDC 2153970: Experimental Crystal Structure Determination

description: Related Article: Guang-Jin Shi, Zhen-Xing Yu, Yue-Dong Wang, Qi Zhang, Shaojin Chen, Liang-Zhong Xu, Kun-Peng Wang, Zhi-Qiang Hu|2022|Dyes Pigm.|205|110531|doi:10.1016/j.dyepig.2022.110531
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2bghmy
title: CCDC 2158890: Experimental Crystal Structure Determination

description: Related Article: Menghe Xu, Yongzheng Liu, Wenmin Pang, Yao Pan, Min Chen, Chen Zou, Chen Tan|2022|Polymer|255|125116|doi:10.1016/j.polymer.2022.125116
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.