cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24518629
title: Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning

description: We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised estimators using the labeled data only. We propose an optimal semi-supervised estimator that can attain this lower bound and therefore improves the supervised estimators, provided that the conditional mean function can be consistently estimated with a proper rate. We further propose a safe semi-supervised estimator. We view it safe, because this estimator is always at least as good as the supervised estimators. We also extend our idea to the aggregation of multiple semi-supervised estimators caused by different misspecifications of the conditional mean function. Extensive numerical simulations and a real data analysis are conducted to illustrate our theoretical results. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24518629.v1
title: Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning

description: We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised estimators using the labeled data only. We propose an optimal semi-supervised estimator that can attain this lower bound and therefore improves the supervised estimators, provided that the conditional mean function can be consistently estimated with a proper rate. We further propose a safe semi-supervised estimator. We view it safe, because this estimator is always at least as good as the supervised estimators. We also extend our idea to the aggregation of multiple semi-supervised estimators caused by different misspecifications of the conditional mean function. Extensive numerical simulations and a real data analysis are conducted to illustrate our theoretical results. Supplementary materials for this article are available online.
DOI: 10.5519/qd.tdo1il81
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (469 records)
DOI: 10.6084/m9.figshare.24943437
title: Chapter 02

description: Data files
DOI: 10.6084/m9.figshare.24943437.v1
title: Chapter 02

description: Data files
DOI: 10.5519/qd.f9sdxv3p
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (469 records)
DOI: 10.18434/mds2-3105
title: escherized-colloids
DOI: 10.5281/zenodo.10456150
title: Results Dataset for Greening America's Rivers: The Potential of Climate Financing for Nature-Based Water Quality Improvement

description: This dataset contains the results from the reserach article titled "Greening America's Rivers: The Potential of Climate Financing for Nature-Based Water Quality Improvement"
DOI: 10.5281/zenodo.10456151
title: Results Dataset for Greening America's Rivers: The Potential of Climate Financing for Nature-Based Water Quality Improvement

description: This dataset contains the results from the reserach article titled "Greening America's Rivers: The Potential of Climate Financing for Nature-Based Water Quality Improvement"
DOI: 10.25417/uic.24891984
title: <b>Faculty Publication Patterns UIC Data</b>

description: As part of ARL’s Research Library Impact Framework initiative, the University of Illinois Chicago (UIC) conducted a study to demonstrate how the availability and use of library collections impacts faculty productivity and publication patterns over time. To address these questions, the project used various statistics: collection size (measured by journal holdings), collection use (measured by number of references in the publications), number of publications by faculty, publication impact (measured by number of citations), number of co-authors, grant funding, page counts, and faculty demographic information. This is the data collected to explore these relationships. The report summarizing the findings: De Groote, Sandra L., Jung Mi Scoulas, Paula R. Dempsey, Deborah D. Blecic, and Felicia A. Barrett. <i>Library Impact Research Report: Faculty Publication Patterns at a Large Urban University and Correlation with Collections Use and Size</i>. Washington, DC: Association of Research Libraries, June 30, 2022.<br>https://doi.org/10.29242/report.uillinoischicago2022.
DOI: 10.25417/uic.24891984.v1
title: <b>Faculty Publication Patterns UIC Data</b>

description: As part of ARL’s Research Library Impact Framework initiative, the University of Illinois Chicago (UIC) conducted a study to demonstrate how the availability and use of library collections impacts faculty productivity and publication patterns over time. To address these questions, the project used various statistics: collection size (measured by journal holdings), collection use (measured by number of references in the publications), number of publications by faculty, publication impact (measured by number of citations), number of co-authors, grant funding, page counts, and faculty demographic information. This is the data collected to explore these relationships. The report summarizing the findings: De Groote, Sandra L., Jung Mi Scoulas, Paula R. Dempsey, Deborah D. Blecic, and Felicia A. Barrett. <i>Library Impact Research Report: Faculty Publication Patterns at a Large Urban University and Correlation with Collections Use and Size</i>. Washington, DC: Association of Research Libraries, June 30, 2022.<br>https://doi.org/10.29242/report.uillinoischicago2022.
DOI: 10.17028/rd.lboro.24942156
title: Supplementary information files for A framework for systematic crystal shape tuning – case of Lovastatin's needle-shaped crystals

description: © the authors, CC-BY 4.0Supplementary files for article A framework for systematic crystal shape tuning – case of Lovastatin's needle-shaped crystalsOne of the most important challenges in the pharmaceutical industry is to produce crystals with desired size and shape distributions, to enhance the critical quality attributes of the drug product, such as efficacy, and to improve manufacturability during downstream processing, such as filtration, drying and granulation. The paper provides a framework for effective crystal shape and size tuning, based on a systematic exploration of standard techniques, such as the linear cooling and supersaturation control (SSC), and novel methods based on the systematic combination of several techniques, namely direct nucleation control (DNC), wet milling, SSC and shape modification additives. The crystallization of lovastatin, which is notorious for its challenging needle-shaped crystals, with an extremely high aspect ratio, was used as a case study, and polypropylene glycol (PPG-4000), at different concentrations, was used as an effective shape modifier from small-scale tests studied previously. The proposed techniques were implemented in the case of seeded and unseeded systems. It was demonstrated that the combination of temperature cycling and polymer additive enhances greatly the control over the aspect ratio and crystal size distribution, compared to conventional linear cooling and SSC strategies. The implementation of wet milling at the beginning of the process, or the introduction of seeds, enhances even further the control of the critical quality attributes of the crystalline product.
DOI: 10.25417/uic.23549247
title: 2022 UIC Library Faculty Survey Data2022 UIC faculty survey questions

description: This data set is a faculty survey data that was collected from UIC faculty between February and March, 2022 to to better understand their needs, resources used, and their perceptions of library resources, services, and roles.There were 12 questions in the 2022 faculty survey, both open-ended and multiple choice. Additionally, faculty's demographics (highest FTE college and faculty rank) and publication data in the form of the number of articles, conference proceedings, books, and book chapters each faculty had published in the past 5 years were obtained from the University’s faculty profile tool and were included in the dataset. This research project was approved by the institutional review board (protocol # 2021-1409)
DOI: 10.25417/uic.23549247.v2
title: 2022 UIC Library Faculty Survey Data2022 UIC faculty survey questions

description: This data set is a faculty survey data that was collected from UIC faculty between February and March, 2022 to to better understand their needs, resources used, and their perceptions of library resources, services, and roles.There were 12 questions in the 2022 faculty survey, both open-ended and multiple choice. Additionally, faculty's demographics (highest FTE college and faculty rank) and publication data in the form of the number of articles, conference proceedings, books, and book chapters each faculty had published in the past 5 years were obtained from the University’s faculty profile tool and were included in the dataset. This research project was approved by the institutional review board (protocol # 2021-1409)
DOI: 10.25417/uic.23549247.v1
title: 2022 UIC Library Faculty Survey Data

description: This data set is a faculty survey data that was collected from UIC faculty between February and March, 2022 to to better understand their needs, resources used, and their perceptions of library resources, services, and roles.There were 12 questions in the 2022 faculty survey, both open-ended and multiple choice. Additionally, faculty's demographics (highest FTE college and faculty rank) and publication data in the form of the number of articles, conference proceedings, books, and book chapters each faculty had published in the past 5 years were obtained from the University’s faculty profile tool and were included in the dataset. This research project was approved by the institutional review board (protocol # 2021-1409)
DOI: 10.5281/zenodo.10459066
title: Annotation files for black the black honey bee genome from "The black honey bee genome: insights on specific structural elements and a first step towards pan-genomes"

description: Annotation files for the AMelMel1.1 black honey bee Apis mellifera mellifera genome assembly described in:

Eynard, Sonia E., Christophe Klopp, Kamila Canale-Tabet, William Marande, Céline Vandecasteele, Céline Roques, Cécile Donnadieu, Quentin Boone, Bertrand Servin, and Alain Vignal. ‘The Black Honey Bee Genome: Insights on Specific Structural Elements and a First Step towards Pan-Genomes’. Preprint. Genomics, 7 December 2023. https://doi.org/10.1101/2023.12.06.570386.

The files were obtained by a liftover approach from the HAv3.1 assembly annotation files, as described in the Github markdown associated with the paper: https://github.com/avignal5/PacificBee/blob/main/AMelMel_3_Compare_Genomes.md
DOI: 10.5281/zenodo.10459067
title: Annotation files for black the black honey bee genome from "The black honey bee genome: insights on specific structural elements and a first step towards pan-genomes"

description: Annotation files for the AMelMel1.1 black honey bee Apis mellifera mellifera genome assembly described in:

Eynard, Sonia E., Christophe Klopp, Kamila Canale-Tabet, William Marande, Céline Vandecasteele, Céline Roques, Cécile Donnadieu, Quentin Boone, Bertrand Servin, and Alain Vignal. ‘The Black Honey Bee Genome: Insights on Specific Structural Elements and a First Step towards Pan-Genomes’. Preprint. Genomics, 7 December 2023. https://doi.org/10.1101/2023.12.06.570386.

The files were obtained by a liftover approach from the HAv3.1 assembly annotation files, as described in the Github markdown associated with the paper: https://github.com/avignal5/PacificBee/blob/main/AMelMel_3_Compare_Genomes.md
DOI: 10.6084/m9.figshare.24943446
title: Research Data Repository: Communication as a Strategy for Promoting Health Literacy in Care Practices

description: This study aimed to analyze the communication process of healthcare professionals for the promotion of health literacy. It is a qualitative study that utilized individual online interviews with 46 healthcare professionals working in Brazil.
DOI: 10.6084/m9.figshare.24943446.v1
title: Research Data Repository: Communication as a Strategy for Promoting Health Literacy in Care Practices

description: This study aimed to analyze the communication process of healthcare professionals for the promotion of health literacy. It is a qualitative study that utilized individual online interviews with 46 healthcare professionals working in Brazil.
DOI: 10.5281/zenodo.7763983
title: OpenSCV: An Open Hierachical Taxonomy for Smart Contract Vulnerabilities – Supplemental Material

description: Dataset and other supplemental material.
DOI: 10.5281/zenodo.10043854
title: ChemM_Hydr_32mix_mzml
DOI: 10.5281/zenodo.10459377
title: OpenSCV: An Open Hierachical Taxonomy for Smart Contract Vulnerabilities – Supplemental Material

description: Dataset and other supplemental material.
DOI: 10.6084/m9.figshare.24943416
title: Chapter 03

description: Data files
DOI: 10.6084/m9.figshare.24943416.v1
title: Chapter 03

description: Data files
DOI: 10.3886/icpsr38404.v1
title: Survey of Consumer Attitudes and Behavior, August 2019
title: Version 1

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter.
The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning.
Other topics explored in this survey were respondents' economic attitudes, home price expectations, savings and financial investments, political affiliation and opinion about the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (Household weight (WT_HH renamed)), <b>WT_HH</b> (Household weight), and <b>WT_AD</b> (Adult weight) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame.
description: Datasets: 
DS0: Study-Level Files
DS1: Public-Use Data
DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Website.
DOI: 10.3886/icpsr38404
title: Survey of Consumer Attitudes and Behavior, August 2019
title: Archival Version

description: The Survey of Consumer Attitudes and Behavior series (also known as the Surveys of Consumers) was undertaken to measure changes in consumer attitudes and expectations, to understand why such changes occur, and to evaluate how they relate to consumer decisions to save, borrow, or make discretionary purchases. The data regularly include the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations. Since the 1940s, these surveys have been produced quarterly through 1977 and monthly thereafter.
The surveys conducted in 2019 focused on topics such as evaluations and expectations about personal finances, employment, price changes, and the national business situation. Opinions were collected regarding respondents' appraisals of present market conditions for purchasing houses, automobiles, computers, and other durables. Also explored in this survey, were respondents' types of savings and financial investments, loan use, family income, and retirement planning.
Other topics explored in this survey were respondents' economic attitudes, home price expectations, savings and financial investments, political affiliation and opinion about the Trump administration. Demographic information includes ethnic origin, sex, age, marital status, and education.
description: The purpose of this survey series is to forecast changes in aggregate consumer behavior.
description: This survey is a rotating panel design. For each monthly sample, an independent cross-section sample of households is drawn. The respondents chosen in this drawing are then reinterviewed six months later. The result is a rotating panel design, and the total sample for any one survey is normally made up of 60 percent new respondents with 40 percent being interviewed for the second time.
description: The data are not weighted, however, this collection contains three weight variables; <b>WT</b> (Household weight (WT_HH renamed)), <b>WT_HH</b> (Household weight), and <b>WT_AD</b> (Adult weight) that must be used in any analysis. For more information on weights and sampling, please refer to the documentation and/or visit the Survey of Consumers Website.
description: National sample of telephone numbers from cell (RDD) sampling frame.
description: Datasets: 
DS0: Study-Level Files
DS1: Public-Use Data
DS2: Restricted-Use Data
description: Persons aged 18 years or older living in households with telephones in the coterminous United States. Smallest Geographic Unit: FIPS code
description: computer-assisted telephone interview (CATI)Information on the Index of Consumer Sentiment, the Index of Current Economic Conditions, and the Index of Consumer Expectations and how they were created can be found in the P.I. Codebook.Additional information on the Surveys of Consumers can be found by visiting the Survey of Consumers Website.
DOI: 10.6084/m9.figshare.24943536
title: CB-AI Survey

description: CB-AI Survey
DOI: 10.6084/m9.figshare.24943536.v1
title: CB-AI Survey

description: CB-AI Survey
DOI: 10.6084/m9.figshare.24943443
title: Chapter 04

description: Data files
DOI: 10.6084/m9.figshare.24943443.v1
title: Chapter 04

description: Data files
DOI: 10.5281/zenodo.10459386
title: Hurricane Katrina Surge Simulations with ADCIRC

description: The zip file contains peak water elevations simulated with ADCIRC using three meteorological forcings (27km, 4.5km and 3km) for Hurricane Katrina in the present-day and future climates.
DOI: 10.5281/zenodo.7126147
title: Hurricane Katrina Surge Simulations with ADCIRC

description: The zip file contains peak water elevations simulated with ADCIRC using three meteorological forcings (27km, 4.5km and 3km) for Hurricane Katrina in the present-day and future climates.
DOI: 10.5281/zenodo.7126148
title: WRF 3.5km North Atlantic August - October 2020

description: TC tracks data Composites of vertical wind shear, potential intensity, 850 hPa absolute vorticity, 850 hPa zonal wind, and 700 hPa specific humidity
DOI: 10.5281/zenodo.10459384
title: SphereCraft - record 1

description: This is record 1 (of a total of 9 records) of the dataset published under the title SphereCraft: A Dataset for Spherical Keypoint Detection, Matching and Camera Pose Estimation. See conference details below.
DOI: 10.5281/zenodo.10459385
title: SphereCraft - record 1

description: This is record 1 (of a total of 9 records) of the dataset published under the title SphereCraft: A Dataset for Spherical Keypoint Detection, Matching and Camera Pose Estimation. See conference details below.
DOI: 10.5517/ccdc.csd.cc23wwsp
title: CCDC 1963223: Experimental Crystal Structure Determination

description: Related Article: Birgit Esser, Przemyslaw Data, Youhei Takeda, Yuta Ikari, Takahito Kaihara, Shimpei Goto, Marcel Bovenkerk, David C. Grenz, Marli Ferreira, Patrycja Stachelek, Takumu Yoshida, Tomoyuki Ikai, Norimitsu Tohnai, Satoshi Minakata|2021|Synthesis|53|1584|doi:10.1055/a-1343-5810
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.cc23wxjg
title: CCDC 1963246: Experimental Crystal Structure Determination

description: Related Article: Birgit Esser, Przemyslaw Data, Youhei Takeda, Yuta Ikari, Takahito Kaihara, Shimpei Goto, Marcel Bovenkerk, David C. Grenz, Marli Ferreira, Patrycja Stachelek, Takumu Yoshida, Tomoyuki Ikai, Norimitsu Tohnai, Satoshi Minakata|2021|Synthesis|53|1584|doi:10.1055/a-1343-5810
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.cc23wxkh
title: CCDC 1963247: Experimental Crystal Structure Determination

description: Related Article: Birgit Esser, Przemyslaw Data, Youhei Takeda, Yuta Ikari, Takahito Kaihara, Shimpei Goto, Marcel Bovenkerk, David C. Grenz, Marli Ferreira, Patrycja Stachelek, Takumu Yoshida, Tomoyuki Ikai, Norimitsu Tohnai, Satoshi Minakata|2021|Synthesis|53|1584|doi:10.1055/a-1343-5810
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.cc249j66
title: CCDC 1975357: Experimental Crystal Structure Determination

description: Related Article: Teng Liu, Chao Huang, Feixiang Cheng, Chixian He, Fan Wang, Xiang Shen, Yongqin Li, Man Lang, Guijun Li|2021|Synthesis|53|518|doi:10.1055/s-0040-1707292
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.