cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5138223
title: Records of reproduction of peacock butterflies in Belgium 2009–2020

description: We show the number of records of reproductive stages (eggs, caterpillars, pupae) per week of the peacock butterfly in Belgium collected during 2009-2020 by citizen scientists on the online wildlife data portal https://observation.org
DOI: 10.15139/s3/xjzonf
title: Replication Data for: Income Inequality and State Parties: Who Gets Represented?

description: Recent studies of representation at the national and state levels have provided evidence that elected officials’ votes, political parties’ platforms, and enacted policy choices are more responsive to the preferences of the affluent, while those with average incomes and the poor have little or no impact in the political process. Yet, this research on the dominance of the affluent has overlooked key partisan differences in the electorate. In this era of hyper-partisanship, we argue that representation occurs through the party system, and we test whether taking this reality into account changes the story of policy dominance by the rich. We combine data on public preferences and state party positions to test for income bias in parties’ representation of their own co-partisans. The results show an interesting pattern in which under-representation of the poor is driven by Democratic parties pushing the more liberal social policy stances of rich Democrats and Republican parties reflecting the particularly conservative economic policy preferences of Rich Republicans. Thus, we have ample evidence that the wealthy, more often than not, do call the shots, but that the degree to which this disproportionate party responsiveness produces less representative policies depends on the party in power and the policy dimension being considered. We conclude by linking this pattern of influence and “coincidental representation” to familiar changes which define the transformation of the New Deal party system.[insert article abstract]
DOI: 10.6092/unibo/amsacta/6735
title: OLEUM Project. Sensory assessment of new artificial olfactory reference materials in virgin olive oil. Performance testing

description: The dataset contains data that validates a procedure for sensory certification of two, recently formulated, artificial olfactory reference materials (RMs) for winey-vinegary and rancid defects of virgin olive oil named “AV8” and “R6”, respectively. Specifically, a protocol for the suitability of RMs has been defined, shared, and applied by six sensory panels involved in the H2020 OLEUM project; it consisted of three steps: i) evaluation of representativeness of RMs (compared to actual samples) and of its intensity values (named “representativeness AV8_part 1 and “representativeness R6_part 1” in the dataset); ii) determination of the panels’ detection threshold (named “threshold AV8_part 2” and “threshold R6_part 2” in the dataset); and iii) evaluation of long-term stability (named “stability AV8_part 3” and “stability R6_part 3” in the dataset).
DOI: 10.25934/00004836.1
title: IPD datapackage for study 'A Randomized, Double-Blind, Placebo-Controlled, Parallel Group, 18-Week Study to Evaluate the Efficacy, Safety, and Tolerability of Canagliflozin in the Treatment of Subjects With Type 2 Diabetes Mellitus With Inadequate Glycemic Control on Metformin Alone or in Combination With a Sulphonylurea' and research request: 'An individual participant data meta-analysis to evaluate a new category of hypoglycemic drugs (SGLT-2Is) on type 2 diabetes mellitus patient sub-groups'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00004780.0
title: Available IPD datapackage for study 'A Randomized, Double-Blind, 3-Arm Parallel-Group, 2-Year (104-Week), Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of JNJ-28431754 Compared With Glimepiride in the Treatment of Subjects With Type 2 Diabetes Mellitus Not Optimally Controlled on Metformin Monotherapy'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00004780.1
title: IPD datapackage for study 'A Randomized, Double-Blind, 3-Arm Parallel-Group, 2-Year (104-Week), Multicenter Study to Evaluate the Efficacy, Safety, and Tolerability of JNJ-28431754 Compared With Glimepiride in the Treatment of Subjects With Type 2 Diabetes Mellitus Not Optimally Controlled on Metformin Monotherapy' and research request: 'An individual participant data meta-analysis to evaluate a new category of hypoglycemic drugs (SGLT-2Is) on type 2 diabetes mellitus patient sub-groups'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.7910/dvn/nsucdr
title: Entscheidungsarchitekturen im Change Management der Polizei – Kognitive Verzerrungseffekte als Hinderer von Veränderungsprozessen bei der Polizei

description: Mit Hilfe dieses Essays werden die Theorien des Change Managements im Verständnis der Verhaltensökonomie aus verschiedenen Blickwinkeln betrachtet und abschließend beurteilt, ob sie als geeignetes Analyseinstrument herangezogen werden können, um damit aktuelle Probleme der Polizei gedanklich zu durchdringen.
DOI: 10.6084/m9.figshare.13616658
title: Neuronized Priors for Bayesian Sparse Linear Regression

description: Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian weight variable and a scale variable transformed from Gaussian via an activation function. Compared with classic spike-and-slab priors, the neuronized priors achieve the same explicit variable selection without employing any latent indicator variables, which results in both more efficient and flexible posterior sampling and more effective posterior modal estimation. Theoretically, we provide specific conditions on the neuronized formulation to achieve the optimal posterior contraction rate, and show that a broadly applicable MCMC algorithm achieves an exponentially fast convergence rate under the neuronized formulation. We also examine various simulated and real data examples and demonstrate that using the neuronization representation is computationally more or comparably efficient than its standard counterpart in all well-known cases. An R package NPrior is provided for using neuronized priors in Bayesian linear regression.
DOI: 10.6084/m9.figshare.13616658.v2
title: Neuronized Priors for Bayesian Sparse Linear Regression

description: Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian weight variable and a scale variable transformed from Gaussian via an activation function. Compared with classic spike-and-slab priors, the neuronized priors achieve the same explicit variable selection without employing any latent indicator variables, which results in both more efficient and flexible posterior sampling and more effective posterior modal estimation. Theoretically, we provide specific conditions on the neuronized formulation to achieve the optimal posterior contraction rate, and show that a broadly applicable MCMC algorithm achieves an exponentially fast convergence rate under the neuronized formulation. We also examine various simulated and real data examples and demonstrate that using the neuronization representation is computationally more or comparably efficient than its standard counterpart in all well-known cases. An R package NPrior is provided for using neuronized priors in Bayesian linear regression.
DOI: 10.6084/m9.figshare.13616658.v1
title: Neuronized Priors for Bayesian Sparse Linear Regression

description: Although Bayesian variable selection methods have been intensively studied, their routine use in practice has not caught up with their non-Bayesian counterparts such as Lasso, likely due to difficulties in both computations and flexibilities of prior choices. To ease these challenges, we propose the neuronized priors to unify and extend some popular shrinkage priors, such as Laplace, Cauchy, horseshoe, and spike-and-slab priors. A neuronized prior can be written as the product of a Gaussian weight variable and a scale variable transformed from Gaussian via an activation function. Compared with classic spike-and-slab priors, the neuronized priors achieve the same explicit variable selection without employing any latent indicator variables, which results in both more efficient and flexible posterior sampling and more effective posterior modal estimation. Theoretically, we provide specific conditions on the neuronized formulation to achieve the optimal posterior contraction rate, and show that a broadly applicable MCMC algorithm achieves an exponentially fast convergence rate under the neuronized formulation. We also examine various simulated and real data examples and demonstrate that using the neuronization representation is computationally more or comparably efficient than its standard counterpart in all well-known cases. An R package NPrior is provided for using neuronized priors in Bayesian linear regression.
DOI: 10.6084/m9.figshare.15057221
title: Augmented reality visualization for craniosynostosis surgery

description: Current approaches for surgical correction of craniosynostosis are highly dependent on the experience of the surgeon and inaccuracies during cranial vault remodelling can compromise the surgical outcome. The objective of this study is the evaluation of a novel augmented reality system to guide surgeons during fronto-orbital advancement procedures. A smartphone software application was developed to overlay virtual models on the surgical field using augmented reality technology. Intraoperative markers and 3D photography are used to ensure accurate alignment between the virtual data and patient anatomy. The system was evaluated through simulations on patient-specific phantoms and the workflow was tested on two real surgeries. Results of this study demonstrate the accuracy of the AR visualisation to guide osteotomy and remodelling during fronto-orbital advancement. Feedback from 9 surgeons supports the integration of this technology on the surgical workflow for the treatment of craniosynostosis to accurately translate the virtual plan into the operating room.
DOI: 10.6084/m9.figshare.15057221.v1
title: Augmented reality visualization for craniosynostosis surgery

description: Current approaches for surgical correction of craniosynostosis are highly dependent on the experience of the surgeon and inaccuracies during cranial vault remodelling can compromise the surgical outcome. The objective of this study is the evaluation of a novel augmented reality system to guide surgeons during fronto-orbital advancement procedures. A smartphone software application was developed to overlay virtual models on the surgical field using augmented reality technology. Intraoperative markers and 3D photography are used to ensure accurate alignment between the virtual data and patient anatomy. The system was evaluated through simulations on patient-specific phantoms and the workflow was tested on two real surgeries. Results of this study demonstrate the accuracy of the AR visualisation to guide osteotomy and remodelling during fronto-orbital advancement. Feedback from 9 surgeons supports the integration of this technology on the surgical workflow for the treatment of craniosynostosis to accurately translate the virtual plan into the operating room.
DOI: 10.5281/zenodo.5138277
title: Data of the effects of temperature and dissolved oxygen on thermal biology and aerobic metabolism of tropical Callinectes sapidus crabs

description: This file contains raw data on the effects of temperature and dissolved oxygen on several aspects of the physiology of the Callinectes sapidus tropical population. I. Data on thermal biology includes the effects of acclimation temperatures on 1) Preferred temperature; 2) Critical thermal minima and maxima; 3) Thermal safety margin (TSM); Thermal metabolic scope (TMS). II. Data on the combined effects of temperature and dissolved oxygen on routine oxygen consumption of C. sapidus. Those data was sent as a scientific paper to the Journal of Thermal Biology.
description: This research is the result of a doctoral degree thesis being carried out in Posgrado en Ciencias del Mar y Limnología at Universidad Autónoma de México (UNAM). The authors are grateful for the support granted to student Adriana García Rueda by the Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship (number CVU: 746562), and for the founds received from the PAPIIT-UNAM program (IN204019) given to CR and from the CONACYT project No. 61503. .
DOI: 10.5281/zenodo.5138276
title: Data of the effects of temperature and dissolved oxygen on thermal biology and aerobic metabolism of tropical Callinectes sapidus crabs

description: This file contains raw data on the effects of temperature and dissolved oxygen on several aspects of the physiology of the Callinectes sapidus tropical population. I. Data on thermal biology includes the effects of acclimation temperatures on 1) Preferred temperature; 2) Critical thermal minima and maxima; 3) Thermal safety margin (TSM); Thermal metabolic scope (TMS). II. Data on the combined effects of temperature and dissolved oxygen on routine oxygen consumption of C. sapidus. Those data was sent as a scientific paper to the Journal of Thermal Biology.
description: This research is the result of a doctoral degree thesis being carried out in Posgrado en Ciencias del Mar y Limnología at Universidad Autónoma de México (UNAM). The authors are grateful for the support granted to student Adriana García Rueda by the Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship (number CVU: 746562), and for the founds received from the PAPIIT-UNAM program (IN204019) given to CR and from the CONACYT project No. 61503. .
DOI: 10.6073/pasta/4097433552819a3c6958b5dbd0b8ef86
title: Freshwater Subtropical Ridge and Slough Wetland Metabolism, South Florida, USA: 2014-2016

description: How climate and habitat drive variation in aquatic metabolism in wetlands remains uncertain. To quantify differences in seasonal aquatic metabolism among wetlands, we estimated aquatic ecosystem metabolism (gross primary productivity, GPP; ecosystem respiration, ER; net aquatic productivity, NAP) in subtropical ridge and slough wetlands of the Florida Everglades from more than 2 years of continuously measured water column dissolved oxygen (DO), photosynthetically active radiation (PAR), water temperature, and water depth. GPP and ER were modeled from light, temperature, and water depth using non-linear minimization and maximum likelihood. Reaeration rates were estimated from wind speed. Dissolved oxygen was below saturation at all sites during both wet and dry seasons. Water depth interacted with vegetation to influence PAR, water temperature, and spatiotemporal patterns in aquatic metabolism. GPP and ER were highest at the slough with lowest submerged aquatic vegetation (low-SAV slough), intermediate in the sawgrass (Cladium jamaicense) ridge site, and lowest at the slough with lowest submerged aquatic vegetation (high-SAV slough). ER was strongly positively correlated with GPP at the sawgrass ridge and low-SAV slough sites. GPP increased with water temperature and PAR across all habitat types, whereas ER decreased (more respiration) with water temperature and PAR. NAP was negatively correlated with water temperature and positively correlated with PAR, suggesting that ER was more sensitive than GPP to water temperature. Aquatic metabolism was largely net heterotrophic in all wetlands, and high-SAV appeared to buffer seasonal variation in PAR and water temperatures that drive NAP in subtropical wetlands. Our results suggest that aquatic ecosystem metabolism in wetlands with seasonal hydrology is sensitive to changes in water depth and vegetation density that influence temperature and light. Expanding our understanding of how metabolic processes and carbon cycling in wetland ecosystems vary across gradients in hydrology, vegetation, and organic matter could enhance our understanding and protection of conditions that maximize carbon storage.
DOI: 10.5281/zenodo.5138288
title: Change in relative abundance of the second generation (third flight peak) of peacock butterflies in Flanders (northern Belgium) since 1950.

description: To investigate historical changes, we calculate for each year with sufficient data the proportional abundance of the third flight peak since 1950 for Flanders (northern part of Belgium) (1950–2008: 26,933 records; 2009–2020: 319,684 records).
DOI: 10.5281/zenodo.5138287
title: Change in relative abundance of the second generation (third flight peak) of peacock butterflies in Flanders (northern Belgium) since 1950.

description: To investigate historical changes, we calculate for each year with sufficient data the proportional abundance of the third flight peak since 1950 for Flanders (northern part of Belgium) (1950–2008: 26,933 records; 2009–2020: 319,684 records).
DOI: 10.6084/m9.figshare.14818311
title: Latent Gaussian Count Time Series

description: This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule–Walker estimation paradigms, based on the autocovariance function of the count series, are developed via a new Hermite expansion. Particle filtering and sequential Monte Carlo methods are used to conduct likelihood estimation. Connections to state space models are made. Our estimation approaches are evaluated in a simulation study and the methods are used to analyze a count series of weekly retail sales. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14818311.v1
title: Latent Gaussian Count Time Series

description: This article develops the theory and methods for modeling a stationary count time series via Gaussian transformations. The techniques use a latent Gaussian process and a distributional transformation to construct stationary series with very flexible correlation features that can have any prespecified marginal distribution, including the classical Poisson, generalized Poisson, negative binomial, and binomial structures. Gaussian pseudo-likelihood and implied Yule–Walker estimation paradigms, based on the autocovariance function of the count series, are developed via a new Hermite expansion. Particle filtering and sequential Monte Carlo methods are used to conduct likelihood estimation. Connections to state space models are made. Our estimation approaches are evaluated in a simulation study and the methods are used to analyze a count series of weekly retail sales. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.14727170
title: Correlation Tensor Decomposition and Its Application in Spatial Imaging Data

description: Multi-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast cancer study, we develop a new tensor learning approach to use pixel-wise correlation information, which is represented through the higher order correlation tensor. We proposed a novel semi-symmetric correlation tensor decomposition method which effectively captures the informative spatial patterns of pixel-wise correlations to facilitate cancer diagnosis. We establish the theoretical properties for recovering structure and for classification consistency. In addition, we develop an efficient algorithm to achieve computational scalability. Our simulation studies and an application on breast cancer imaging data all indicate that the proposed method outperforms other competing methods in terms of pattern recognition and prediction accuracy.
DOI: 10.6084/m9.figshare.14727170.v1
title: Correlation Tensor Decomposition and Its Application in Spatial Imaging Data

description: Multi-dimensional tensor data have gained increasing attention in the recent years, especially in biomedical imaging analyses. However, the most existing tensor models are only based on the mean information of imaging pixels. Motivated by multimodal optical imaging data in a breast cancer study, we develop a new tensor learning approach to use pixel-wise correlation information, which is represented through the higher order correlation tensor. We proposed a novel semi-symmetric correlation tensor decomposition method which effectively captures the informative spatial patterns of pixel-wise correlations to facilitate cancer diagnosis. We establish the theoretical properties for recovering structure and for classification consistency. In addition, we develop an efficient algorithm to achieve computational scalability. Our simulation studies and an application on breast cancer imaging data all indicate that the proposed method outperforms other competing methods in terms of pattern recognition and prediction accuracy.