cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8026501
title: Mobile Application Privacy Risk Assessments from User-authored Scenarios

description: Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in<br> the context of four user-authored scenarios.
DOI: 10.5281/zenodo.8061495
title: Mobile Application Privacy Risk Assessments from User-authored Scenarios

description: Mobile applications (apps) provide users valuable benefits at the risk of exposing users to privacy harms. Improving privacy in mobile apps faces several challenges, in particular, that many apps are developed by low resourced software development teams, such as end-user programmers or in startups. In addition, privacy risks are primarily known to users, which can make it difficult for developers to prioritize privacy for sensitive data. In this paper, we introduce a novel, lightweight method that allows app developers to elicit scenarios and privacy risk scores from users directly using only an app screenshot. The technique relies on named entity recognition (NER) to identify information types in user-authored scenarios, which are then fed in real-time to a privacy risk survey that users complete. The best-performing NER model predicts information types with a weighted average precision of 0.70 and recall of 0.72, after post-processing to remove false positives. The model was trained on a labeled 300-scenario corpus, and evaluated in an end-to-end evaluation using an additional 203 scenarios yielding 2,338 user-provided privacy risk scores. Finally, we discuss how developers can use the risk scores to prioritize, select and apply privacy design strategies in<br> the context of four user-authored scenarios.
DOI: 10.25810/yr7w-5c22.8
title: Korean Newspaper Coverage of Climate Change or Global Warming, 2000-2023 - May 2023

description: The Media and Climate Change Observatory Data monitors 130 sources (across newspapers, radio and TV) in 59 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.3886/e170141v1
title: Prison Agriculture in the United States

description: The Prison Agriculture Lab at Colorado State University compiled a first-of-a-kind nationwide data set tracking prison agriculture in the United States. The data set identifies 1101 adult state-operated prisons, including 662 adult state-operated prisons with some type of crops and silviculture; horticulture and landscaping; animal agriculture; and/or food processing and production. These four types of agriculture are further broken down into subtypes, which are more detailed descriptions of an agricultural activity. The data set furthermore entails details on the specific drivers of agriculture, which are the goals or justifications for agriculture at each prison provided by prison authorities. The drivers are broadly classified into financial, idleness reduction, training, or reparative driver groups. Data covers all 50 states and was collected between 2019-2022 by speaking with prison authorities and by scraping official government and agency reports and websites. To enrich the geospatial aspects of this data set, the prison agriculture data provides a key to link with Homeland Infrastructure Foundation-Level prison boundaries data from 2020.
description: Response Rates: We have confirmed data on agricultural activities for 662 out of 1101 adult government-run state prisons.<br>
description: The data collection sought to include all members of the universe.
description: Adult (18+) government-run state prisons in the United States. Smallest Geographic Unit: Individual prison
description: other; telephone interview; web scraping;
DOI: 10.3886/e170141v2
title: Prison Agriculture in the United States

description: The Prison Agriculture Lab at Colorado State University compiled a first-of-a-kind nationwide data set tracking prison agriculture in the United States. The data set identifies 1101 adult state-operated prisons, including 662 adult state-operated prisons with some type of crops and silviculture; horticulture and landscaping; animal agriculture; and/or food processing and production. These four types of agriculture are further broken down into subtypes, which are more detailed descriptions of an agricultural activity. The data set furthermore entails details on the specific drivers of agriculture, which are the goals or justifications for agriculture at each prison provided by prison authorities. The drivers are broadly classified into financial, idleness reduction, training, or reparative driver groups. Data covers all 50 states and was collected between 2019-2022 by speaking with prison authorities and by scraping official government and agency reports and websites. To enrich the geospatial aspects of this data set, the prison agriculture data provides a key to link with Homeland Infrastructure Foundation-Level prison boundaries data from 2020.
description: Response Rates: We have confirmed data on agricultural activities for 662 out of 1101 adult government-run state prisons.<br>
description: The data collection sought to include all members of the universe.
description: Adult (18+) government-run state prisons in the United States. Smallest Geographic Unit: Individual prison
description: other; telephone interview; web scraping;
DOI: 10.5517/ccdc.csd.cc2fhcp0
title: CCDC 2249102: Experimental Crystal Structure Determination

description: Related Article: Andres Arias-Gómez, Mario A. Macías, Jaime Portilla|2023|RSC Advances|13|16377|doi:10.1039/D3RA02553E
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.cc2fhgjy
title: CCDC 2249190: Experimental Crystal Structure Determination

description: Related Article: Bang-Heng Lyu, Ze-Yu Ruan, Wen Cui, Si-Guo Wu, Zhao-Ping Ni, Ming-Liang Tong|2023|Inorg.Chem.Front.|10|3577|doi:10.1039/D3QI00644A
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.cc2fjbdq
title: CCDC 2250023: Experimental Crystal Structure Determination

description: Related Article: Simone Adorinni, Giulio Goti, Lorenzo Rizzo, Federica Grassi, Slavko Kralj, Fatima Matroodi, Mirco Natali, Rita De Zorzi, Silvia Marchesan, Luca Dell’Amico|2023|Chem.Commun.|59|7619|doi:10.1039/D3CC01673K
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.cc2fk023
title: CCDC 2250664: Experimental Crystal Structure Determination

description: Related Article: Fabian Seifert, Helmar Görls, Stephan Kupfer, Robert Kretschmer|2023|Chem.Commun.|59|7627|doi:10.1039/D3CC01608K
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.cc2fk034
title: CCDC 2250665: Experimental Crystal Structure Determination

description: Related Article: Fabian Seifert, Helmar Görls, Stephan Kupfer, Robert Kretschmer|2023|Chem.Commun.|59|7627|doi:10.1039/D3CC01608K
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.cc2fldyd
title: CCDC 2252024: Experimental Crystal Structure Determination

description: Related Article: T.N. Lohith, M.K. Hema, C.S. Karthik, Sandeep S, R. Jothi Ramalingam, Muthusamy Karnan, N.K. Lokanath, L. Mallesha, P. Mallu, M.A. Sridhar|2023|J.Mol.Struct.|1289|135841|doi:10.1016/j.molstruc.2023.135841
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.cc2flm5v
title: CCDC 2252217: Experimental Crystal Structure Determination

description: Related Article: Jupally Prashanth, Artem O. Surov, Ksenia V. Drozd, German L. Perlovich, Sridhar Balasubramanian|2023|CrystEngComm|25|3501|doi:10.1039/D3CE00328K
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.cc2flm6w
title: CCDC 2252218: Experimental Crystal Structure Determination

description: Related Article: Jupally Prashanth, Artem O. Surov, Ksenia V. Drozd, German L. Perlovich, Sridhar Balasubramanian|2023|CrystEngComm|25|3501|doi:10.1039/D3CE00328K
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.cc2flm7x
title: CCDC 2252219: Experimental Crystal Structure Determination

description: Related Article: Jupally Prashanth, Artem O. Surov, Ksenia V. Drozd, German L. Perlovich, Sridhar Balasubramanian|2023|CrystEngComm|25|3501|doi:10.1039/D3CE00328K
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.cc2flm8y
title: CCDC 2252220: Experimental Crystal Structure Determination

description: Related Article: Jupally Prashanth, Artem O. Surov, Ksenia V. Drozd, German L. Perlovich, Sridhar Balasubramanian|2023|CrystEngComm|25|3501|doi:10.1039/D3CE00328K
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: 10.5281/zenodo.8061520
title: BigWig files for HSC lncRNAs project

description: BigWig files for HSC lncRNAs project
DOI: 10.5281/zenodo.8061519
title: BigWig files for HSC lncRNAs project

description: BigWig files for HSC lncRNAs project
DOI: 10.5281/zenodo.8061222
title: processed single-cell data from "Cancer cell non-autonomous tumor progression from chromosomal instability"

description: The h5ad files can be used as processed scRNA-seq input to the ContactTracing code (https://zenodo.org/badge/latestdoi/625036312). There is one file for the highCIN/lowCIN comparison, and another for the highCIN/noSTING comparison.
description: Supported by grants from National Cancer Institute: R01 CA280414-01 R01 CA256188-01 R01 CA280572-01 R21 CA266660-01
DOI: 10.5281/zenodo.8061221
title: processed single-cell data from "Cancer cell non-autonomous tumor progression from chromosomal instability"

description: The h5ad files can be used as processed scRNA-seq input to the ContactTracing code (https://zenodo.org/badge/latestdoi/625036312). There is one file for the highCIN/lowCIN comparison, and another for the highCIN/noSTING comparison.
description: Supported by grants from National Cancer Institute: R01 CA280414-01 R01 CA256188-01 R01 CA280572-01 R21 CA266660-01
DOI: 10.5281/zenodo.8060807
title: CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)

description: CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
description: This work was supported 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. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.
description: {"references": ["Sarzaeim, P., Munoz-Arriola, F., Jarquin, D., Aslam, H., and De Leon Gatti, N.: CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the U.S. and Canada, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-11, in review, 2023.", "Sarzaeim, Parisa, Aslam, Hasnat, & Munoz-Arriola, Francisco. (2023). CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7490246"]}
DOI: 10.5281/zenodo.8060806
title: CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (v2.0)

description: CLIM4OMICS Analytics and Database is Improved database of G2F data repository that contains OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2021. The goal of this pipeline is to aggregate, improve, and synthesize multi-dimensional G2F data including Geno-type, Phenotype and Environmental data for GxE modeling. This dataset contains 79,122 phenotype measurements, 378 genotypes of maize lines, environmental data of 178 locations and Python Scripts for Quality control (QC), Consistency control (CC) steps and ML models for GxE interactions. The Environmental data is extracted from NWS, DayMet and NSRDB databases and processed for QC and CC. The environmental dataset contains the minimum temperature (<em>T<sub>min</sub></em>)<em>,</em> average temperature (<em>T<sub>mean</sub></em>)<em>, </em>maximum temperature (<em>T<sub>max</sub></em>)<em>,</em> minimum dew point (<em>DP<sub>min</sub></em>)<em>,</em> average dew point (<em>DP<sub>mean</sub></em>)<em>, </em>maximum dew point (<em>DP<sub>max</sub></em>)<em>, </em>minimum relative humidity (<em>RH<sub>min</sub></em>)<em>, </em>average relative humidity (<em>RH<sub>mean</sub></em>)<em>, </em>maximum relative humidity (<em>RH<sub>max</sub></em>)<em>, </em>minimum solar radiation (<em>SR<sub>min</sub></em>)<em>, </em>average solar radiation (<em>SR<sub>mean</sub></em>)<em>, </em>maximum solar radiation (<em>SR<sub>max</sub></em>)<em>, </em>accumulative rainfall (<em>R<sub>acc</sub></em>)<em>, </em>average wind speed (<em>WS<sub>mean</sub></em>), and average wind direction (<em>WD<sub>mean</sub></em>). This package also contains the raw G2F data and preprocessing pipeline.
description: This work was supported 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. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database; and Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln.
description: {"references": ["Sarzaeim, P., Munoz-Arriola, F., Jarquin, D., Aslam, H., and De Leon Gatti, N.: CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the U.S. and Canada, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-11, in review, 2023.", "Sarzaeim, Parisa, Aslam, Hasnat, & Munoz-Arriola, Francisco. (2023). CLImate for Maize OMICS: CLIM4OMICS Analytics and Database (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7490246"]}
DOI: 10.25739/abf6-pa81
title: Carolyn_Lawrence_Dill_GOMAP_Maize_MaizeGDB_B97_NAM_1.0_October_2022.r1

description: This dataset generated by GOMAP is a high-coverage and reproducible functional annotation set based on Gene Ontology (GO) term assignments that covers all gene models in MaizeGDB Zea mays B97 genome with a median of 10 annotations per gene model with 99.99% coverage. It was created in October of 2022.
DOI: 10.6084/m9.figshare.11369073
title: Experiment data

description: Data used in the experiments of the study. Both the original DEM and its derived digital terrain indices are available. Ditch labels used in the experiments with a width of 3 meters are also available.
DOI: 10.6084/m9.figshare.11369073.v1
title: Experiment data

description: Data used in the experiments of the study. Both the original DEM and its derived digital terrain indices are available. Ditch labels used in the experiments with a width of 3 meters are also available.
DOI: 10.7278/s50d-fdna-tekm
title: Data for: Prediction of Atypical Left Atrial Flutter
DOI: 10.6084/m9.figshare.22756140
title: On the Use of Minimum Penalties in Statistical Learning

description: Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not generalize to other types of models. In this article we proposed the MinPen framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using common assumptions. The MinPen framework uses a novel penalty based on the minimum function to simultaneously detect and exploit relationships between responses. An iterative algorithm is proposed as a solution to the nonconvex optimization. Theoretical results such as high-dimensional convergence rates, model selection consistency, and a framework for post selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses. Tuning parameter selection is also addressed. Finally, simulations and two data examples are presented to show the finite sample properties of this framework. Supplemental material providing proofs, additional simulations, code, and datasets are available online.
DOI: 10.6084/m9.figshare.22756140.v1
title: On the Use of Minimum Penalties in Statistical Learning

description: Modern multivariate machine learning and statistical methodologies estimate parameters of interest while leveraging prior knowledge of the association between outcome variables. The methods that do allow for estimation of relationships do so typically through an error covariance matrix in multivariate regression which does not generalize to other types of models. In this article we proposed the MinPen framework to simultaneously estimate regression coefficients associated with the multivariate regression model and the relationships between outcome variables using common assumptions. The MinPen framework uses a novel penalty based on the minimum function to simultaneously detect and exploit relationships between responses. An iterative algorithm is proposed as a solution to the nonconvex optimization. Theoretical results such as high-dimensional convergence rates, model selection consistency, and a framework for post selection inference are provided. We extend the proposed MinPen framework to other exponential family loss functions, with a specific focus on multiple binomial responses. Tuning parameter selection is also addressed. Finally, simulations and two data examples are presented to show the finite sample properties of this framework. Supplemental material providing proofs, additional simulations, code, and datasets are available online.
DOI: 10.25921/8bv7-mc62
title: EM710 Water Column Sonar Data Collected During NF1602-Transit