cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6697642
title: The COVID-related mental health load of neonatal healthcare professionals: a multicenter study in Italy

description: This database includes the raw data linked with the paper “The COVID-related mental health load of neonatal healthcare professionals: a multicenter study in Italy”. This study is part of the Staff and Parental Adjustment to COVID-19 Epidemics – Neonatal Experience in Tuscany” (SPACE-NET) multicenter project. In this paper, we report data on the mental health load experienced by physicians, nurses and other healthcare professionals who work in neonatal wards (NWs) and neonatal intensive care units (NICUs). <strong>Procedures</strong> All healthcare personnel of seven level-3 and six level-2 neonatal units in Tuscany (Italy) were invited to complete an online survey. We measured the level of physical exposure to COVID-19 risk, self-reported pandemic-related stress, and mental health load outcomes (i.e., anxiety, depression, burnout, psychosomatic, and post-traumatic symptoms) using validated, self-administered questionnaires. <strong>Analytical plan</strong> For variables depression, anxiety, burnout, post-traumatic symptoms a dichotomous variable [above the clinical cutoff/below the cutoff] was computed, and binomial regressions were used to estimate the probability of scoring above cutoff, including the following predictors in the model: setting, job, job experience, and <em>pandemic-related stress index</em>. As all mental health domains were correlated, in order to reduce the number of statistical comparisons and obtain an overall index, we used a principal component analysis (PCA) to calculate a global <em>mental health load index </em>(<em>MHLI</em>). <strong>Findings in brief</strong> Scores above the clinical cutoff were reported by 91% of participants for symptoms of anxiety, 29% for post-traumatic symptoms, 13% for burnout, and 3% for symptoms of depression. Pandemic-related stress was significantly associated with all the measured mental health load outcomes, with a Risk Ratio of 3.33 for clinically relevant anxiety, 2.39 for post-traumatic symptoms, 1.79 for emotional exhaustion, and 2.51 for depression.
description: Funding: bando ricerca Regione Toscana COVID-19
DOI: 10.5281/zenodo.6697641
title: The COVID-related mental health load of neonatal healthcare professionals: a multicenter study in Italy

description: This database includes the raw data linked with the paper “The COVID-related mental health load of neonatal healthcare professionals: a multicenter study in Italy”. This study is part of the Staff and Parental Adjustment to COVID-19 Epidemics – Neonatal Experience in Tuscany” (SPACE-NET) multicenter project. In this paper, we report data on the mental health load experienced by physicians, nurses and other healthcare professionals who work in neonatal wards (NWs) and neonatal intensive care units (NICUs). <strong>Procedures</strong> All healthcare personnel of seven level-3 and six level-2 neonatal units in Tuscany (Italy) were invited to complete an online survey. We measured the level of physical exposure to COVID-19 risk, self-reported pandemic-related stress, and mental health load outcomes (i.e., anxiety, depression, burnout, psychosomatic, and post-traumatic symptoms) using validated, self-administered questionnaires. <strong>Analytical plan</strong> For variables depression, anxiety, burnout, post-traumatic symptoms a dichotomous variable [above the clinical cutoff/below the cutoff] was computed, and binomial regressions were used to estimate the probability of scoring above cutoff, including the following predictors in the model: setting, job, job experience, and <em>pandemic-related stress index</em>. As all mental health domains were correlated, in order to reduce the number of statistical comparisons and obtain an overall index, we used a principal component analysis (PCA) to calculate a global <em>mental health load index </em>(<em>MHLI</em>). <strong>Findings in brief</strong> Scores above the clinical cutoff were reported by 91% of participants for symptoms of anxiety, 29% for post-traumatic symptoms, 13% for burnout, and 3% for symptoms of depression. Pandemic-related stress was significantly associated with all the measured mental health load outcomes, with a Risk Ratio of 3.33 for clinically relevant anxiety, 2.39 for post-traumatic symptoms, 1.79 for emotional exhaustion, and 2.51 for depression.
description: Funding: bando ricerca Regione Toscana COVID-19
DOI: 10.7284/129024
title: Anemometer data collected during research cruise CB1083 using a RMYoung 05106 instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129026
title: Barometer data collected during research cruise CB1083 using a RMYoung 61302 instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129023
title: CTD (Conductivity, Temperature, Depth) data collected during research cruise CB1083 using a SeaBird SBE-911+ instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129030
title: GNSS (Global Navigation Satellite System) data collected during research cruise CB1083 using a Furuno GP-320B instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129025
title: Hygrometer data collected during research cruise CB1083 using a RMYoung 41382 instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129027
title: Radiometer data collected during research cruise CB1083 using a Biospherical QSP-2150 instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129028
title: Singlebeam Echosounder data collected during research cruise CB1083 using a Knudsen 320M instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/129029
title: TSG (Thermosalinograph) data collected during research cruise CB1083 using a SeaBird SBE-21 instrument system onboard the platform RV Clifford A. Barnes

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.20425488
title: RheometerTestData_mk_et_al.xlsx

description: RheometerTestData
DOI: 10.6084/m9.figshare.20425488.v1
title: RheometerTestData_mk_et_al.xlsx

description: RheometerTestData
DOI: 10.6084/m9.figshare.20425383
title: Ruthsatz_et_al_2022_Timing of parental breeding influences pollution sensitivity.xlsx

description: Parents: Data of adults R.t_container_embryo: Data for all eggs RT_embryo: Data for all hatchlings RT_metamorphs: Data for larvae at Gosner stage 42 RT_jueveniles: Data for juveniles <br>
DOI: 10.6084/m9.figshare.20425383.v3
title: Ruthsatz_et_al_2022_Timing of parental breeding influences pollution sensitivity.xlsx

description: Parents: Data of adults R.t_container_embryo: Data for all eggs RT_embryo: Data for all hatchlings RT_metamorphs: Data for larvae at Gosner stage 42 RT_jueveniles: Data for juveniles <br>
DOI: 10.6084/m9.figshare.20425383.v2
title: Ruthsatz_et_al_2022_Timing of parental breeding influences pollution sensitivity.xlsx

description: Parents: Data of adults R.t_container_embryo: Data for all eggs RT_embryo: Data for all hatchlings RT_metamorphs: Data for larvae at Gosner stage 42 RT_jueveniles: Data for juveniles <br>
DOI: 10.6084/m9.figshare.13611285.v9
title: A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data

description: Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration. Mismatched pairs can considerably impact the model fit and disrupt the estimation of regression parameters. In this article, we present a method to adjust for such mismatches under “partial shuffling” in which a sufficiently large fraction of (predictors, response)-pairs are observed in their correct correspondence. The proposed approach is based on a pseudo-likelihood in which each term takes the form of a two-component mixture density. expectation-maximization schemes are proposed for optimization, which (i) scale favorably in the number of samples, and (ii) achieve excellent statistical performance relative to an oracle that has access to the correct pairings as certified by simulations and case studies. In particular, the proposed approach can tolerate considerably larger fraction of mismatches than existing approaches, and enables estimation of the noise level as well as the fraction of mismatches. Inference for the resulting estimator (standard errors, confidence intervals) can be based on established theory for composite likelihood estimation. Along the way, we also propose a statistical test for the presence of mismatches and establish its consistency under suitable conditions. Supplemental files for this article are available online.
DOI: 10.5281/zenodo.6901524
title: Frequency ratio of the Ar13+ clock transition and the 171Yb+ ocutpole transition

description: Measured and corrected frequency ratio between the optical clock transition in Ar<sup>13+</sup> and the octupole transition in <sup>171</sup>Yb<sup>+</sup>. Data is provided for the two isotopes <sup>40</sup>Ar<sup>13+</sup> and <sup>36</sup>Ar<sup>13+</sup>.
DOI: 10.5281/zenodo.6901523
title: Frequency ratio of the Ar13+ clock transition and the 171Yb+ ocutpole transition

description: Measured and corrected frequency ratio between the optical clock transition in Ar<sup>13+</sup> and the octupole transition in <sup>171</sup>Yb<sup>+</sup>. Data is provided for the two isotopes <sup>40</sup>Ar<sup>13+</sup> and <sup>36</sup>Ar<sup>13+</sup>.
DOI: 10.5281/zenodo.6958808
title: A topic modelling application for violence against the women through text mining techniques

description: This research was focused on a social problem that affects worldwide. Violence Against Women (VAW) and girls occurs in many forms with different levels of aggressiveness. There is a wide amount of data on the internet about real cases of VAW that can be gathered for further study. Many researchers have done studies on structured data sets which facilitates their manipulation and understanding. But in this work digital news (data source large non-structured text) was used. These are the data used for the development of the investigation.This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.
DOI: 10.5281/zenodo.6958807
title: A topic modelling application for violence against the women through text mining techniques

description: This research was focused on a social problem that affects worldwide. Violence Against Women (VAW) and girls occurs in many forms with different levels of aggressiveness. There is a wide amount of data on the internet about real cases of VAW that can be gathered for further study. Many researchers have done studies on structured data sets which facilitates their manipulation and understanding. But in this work digital news (data source large non-structured text) was used. These are the data used for the development of the investigation.This file was extracted from our mongoDB database. The data set contains real news of violence against women, which were organized with their date, the title and the body of the news.
DOI: 10.6084/m9.figshare.16958635
title: Comparing Three Groups

description: For multiple comparisons in analysis of variance, the practitioners’ handbooks generally advocate standard methods such as Bonferroni, or an <i>F</i>-test followed by Tukey’s honest significant difference method. These methods are known to be suboptimal compared to closed testing procedures, but improved methods can be complex in the general multigroup set-up. In this note, we argue that the case of three-groups is special: with three groups, closed testing procedures are powerful and easy to use. We describe four different closed testing procedures specifically for the three-group set-up. The choice of method should be determined by assessing which of the comparisons are considered primary and which are secondary, as dictated by subject-matter considerations. We describe how all four methods can be used with any standard software.
DOI: 10.6084/m9.figshare.16958635.v2
title: Comparing Three Groups

description: For multiple comparisons in analysis of variance, the practitioners’ handbooks generally advocate standard methods such as Bonferroni, or an <i>F</i>-test followed by Tukey’s honest significant difference method. These methods are known to be suboptimal compared to closed testing procedures, but improved methods can be complex in the general multigroup set-up. In this note, we argue that the case of three-groups is special: with three groups, closed testing procedures are powerful and easy to use. We describe four different closed testing procedures specifically for the three-group set-up. The choice of method should be determined by assessing which of the comparisons are considered primary and which are secondary, as dictated by subject-matter considerations. We describe how all four methods can be used with any standard software.
DOI: 10.6084/m9.figshare.16964783.v2
title: Statistical Implications of Endogeneity Induced by Residential Segregation in Small-Area Modeling of Health Inequities

description: Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity.” We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.
DOI: 10.6084/m9.figshare.16567327
title: Pseudo-Ranks: The Better Way of Ranking?

description: Rank-based methods are frequently used in the life sciences, and in the empirical sciences in general. Among the best-known examples of nonparametric rank-based tests are the Wilcoxon-Mann-Whitney test and the Kruskal–Wallis test. However, recently, potential pitfalls and paradoxical results pertaining to the use of traditional rank-based procedures for more than two samples have been highlighted, and the so-called pseudo-ranks have been proposed as a remedy for this type of problems. The aim of the present article is twofold: First, we show that pseudo-ranks might also behave counterintuitively when splitting up groups. Second, since the use of pseudo-ranks leads to a slightly different interpretation of the results, we provide some guidance regarding the decision for one or the other approach, in particular with respect to interpretability and generalizability of the findings. It turns out that the choice of the reference distribution, to which the individual groups are compared, is crucial. The practically relevant implications of these aspects are illustrated by a discussion of a dataset from epilepsy research. Summing up, one should decide based on thorough case-by-case considerations whether ranks or pseudo-ranks are appropriate.
DOI: 10.6084/m9.figshare.16958635.v1
title: Comparing three groups

description: For multiple comparisons in analysis of variance, practitioners’ handbooks generally advocate standard methods such as Bonferroni, or an <i>F</i>-test followed by Tukey’s honest significant difference method. These methods are known to be suboptimal compared to closed testing procedures, but improved methods can be complex in the general multi-group set-up. In this note we argue that the case of three-groups is special: with three groups, closed testing procedures are powerful and easy to use. We describe four different closed testing procedures specifically for the three-group set-up. The choice of method should be determined by assessing which of the comparisons are considered primary and which are secondary, as dictated by subject-matter considerations. We describe how all four methods can be used with any standard software.
DOI: 10.6084/m9.figshare.16964783
title: Statistical Implications of Endogeneity Induced by Residential Segregation in Small-Area Modeling of Health Inequities

description: Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity.” We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.
DOI: 10.6084/m9.figshare.16567327.v1
title: Pseudo-Ranks: The Better Way of Ranking?

description: Rank-based methods are frequently used in the life sciences, and in the empirical sciences in general. Among the best-known examples of nonparametric rank-based tests are the Wilcoxon-Mann-Whitney test and the Kruskal-Wallis test. However, recently, potential pitfalls and paradoxical results pertaining to the use of traditional rank-based procedures for more than two samples have been highlighted, and so-called pseudo-ranks have been proposed as a remedy for this type of problems. The aim of the present manuscript is twofold: Firstly, we show that pseudo-ranks might also behave counterintuitively when splitting up groups. Secondly, since the use of pseudo-ranks leads to a slightly different interpretation of the results, we provide some guidance regarding the decision for one or the other approach, in particular with respect to interpretability and generalizability of the findings. It turns out that the choice of the reference distribution, to which the individual groups are compared, is crucial. The practically relevant implications of these aspects are illustrated by a discussion of a dataset from epilepsy research. Summing up, one should decide based on thorough case-by-case considerations whether ranks or pseudo-ranks are appropriate.
DOI: 10.6084/m9.figshare.16964783.v1
title: Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities

description: Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model’s offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as “offset endogeneity”. We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.
DOI: 10.6084/m9.figshare.17096644
title: Supporting STEM transfer students through cross-institutional undergraduate research experiences

description: The impact and benefits of undergraduate research experiences (URE) in the first two years of post-secondary education have been well documented. Early exposure to research experiences is effective in recruitment, diversity and persistence in Science, Technology, Engineering and Mathematics (STEM) degree programs, particularly among underrepresented minority students. The Summer Undergraduate Research Experience Course (SUREC) presented in this paper is a cross-institutional collaboration between a two-year college (2YC) and a four-year college (4YC), designed to expose 2YC transfer students to a rigorous 12-week URE focused on geosciences. Findings from the program evaluation indicate the SUREC increased transfer student’s STEM knowledge and skills, science identity, and self-efficacy. Twenty of the twenty-four SUREC participants, representing underrepresented and majority students alike, transferred to a 4YC to continue in geosciences or in a related STEM field within two-years of the URE. A strong cross-institutional collaboration that combined academic, financial, and mentoring support was key to enhancing transfer students’ URE experiences. Leveraging teaching and research expertise of 2YC adjuncts as emerging academics and educators was a hallmark of this program. Supplemental data for this article is available online at https://doi.org/10.1080/10899995.2021.2005510