cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26392277.v1
title: How young children use manifest emotions and dominance cues to understand social rules: a registered report

description: Given the complexity of human social life, it is astonishing to observe how quickly children adapt to their social environment. To be accepted by the other members, it is crucial to understand and follow the rules and norms shared by the group. How and from whom do young children learn these social rules? In the experiments, based on the crucial role of affective social learning and dominance hierarchies in simple rule understanding, we showed 15-to-23-month-olds and 3-to-5-year-old children videos where the agents’ body size and affective cues were manipulated. In the <i>dominant rule-maker condition</i>, when a smaller protagonist puts an object in one location, a bigger agent reacts with a positive reaction; on the contrary, when the smaller protagonist puts an object in another location, the bigger agent displays a negative reaction. In the <i>subordinate rule-maker condition</i>, the roles are shifted but the agents differ. Toddlers expect the protagonist to follow the rules (based on anticipatory looks), independent of the dominant status of the rule-making agent. Three-to-five-year-old pre-schoolers overall perform at the chance level but expect the protagonist to disobey a rule in the first trial, and obey the rule in the second trial if the rule-maker is dominant.
DOI: 10.15468/dl.25buu5
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sorbus austriaca Gruppe)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 10 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040684-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gbed7x
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sorbus glabriuscula Mc All.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 16 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040685-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ps9twz
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Sorbus graeca Gruppe)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 11 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040686-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.78p667
title: Occurrence Download

description: A dataset containing 19918 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hedlundia mougeotii (Soy.-Will. & Godr.) Sennikov & Kurtto)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 19918 records from 150 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040687-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13119938
title: Data from "Impact-induced hydrothermal conditions in complex craters on Mars"

description: Supplementary materials for the paper "Impact-induced hydrothermal conditions in complex craters on Mars". It includes iSALE input files for simulations presented in Figures 1, 2, 5, S1, S2, S3, and S4. It also includes select data processing and plotting files. Refer to readme.txt file for instructions.
DOI: 10.5281/zenodo.13119939
title: Data from "Impact-induced hydrothermal conditions in complex craters on Mars"

description: Supplementary materials for the paper "Impact-induced hydrothermal conditions in complex craters on Mars". It includes iSALE input files for simulations presented in Figures 1, 2, 5, S1, S2, S3, and S4. It also includes select data processing and plotting files. Refer to readme.txt file for instructions.
DOI: 10.5281/zenodo.13119898
title: SOSE output analyzed in "Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean" (Part 2-5)

description: SOSE output analyzed in "Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean" submitted to GMD. Complete theta data is 174GB. Thus, this dataset is separated into smaller files. These data can be accessed by accessing Zenode links (see below) or downloading from  http://sose.ucsd.edu/.
DOI: 10.5281/zenodo.13119899
title: SOSE output analyzed in "Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean" (Part 2-5)

description: SOSE output analyzed in "Evaluation of MITgcm-based ocean reanalysis for the Southern Ocean" submitted to GMD. Complete theta data is 174GB. Thus, this dataset is separated into smaller files. These data can be accessed by accessing Zenode links (see below) or downloading from  http://sose.ucsd.edu/.
DOI: 10.6084/m9.figshare.25951378
title: Counterfactual Mediation Analysis with a Latent Class Exposure

description: Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method “updated pseudo class draws (uPCD)” to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome (“one-step,” “bias-adjusted three-step,” “modal class assignment,” “non-inclusive pseudo class draws,” and “inclusive pseudo class draws”) and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.
DOI: 10.6084/m9.figshare.25567591
title: Understanding Composite-Based Structural Equation Modeling Methods From the Perspective of Regression Component Analysis

description: Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model’s observed variables. The weight matrix may be calculated from the factor model’s parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors. Analytically, RCA equates to modeling with “regression method” factor scores, except that, while those scores will be inconsistent with the original factor model, they are strictly consistent with the RCA model. When the original factor model is strictly correct in the population and the composites in RCA are standardized, RCA parameter estimates replicate those from regression-weighted forms of partial least squares (PLS) path modeling and generalized structured component analysis (GSCA)—affirming that those methods also equate to modeling with regression method factor scores under the same conditions. Parallel measurement allows RCA to replicate both correlation weight and regression weight versions of PLS and GSCA. These results suggest that RCA and regression-weighted forms of PLS and GSCA are all consistent approaches for modeling data that conforms to a factor model. All analytical methods are described using one consistent symbol palette. Complete R syntax is provided.
DOI: 10.6084/m9.figshare.25567591.v1
title: Understanding Composite-Based Structural Equation Modeling Methods From the Perspective of Regression Component Analysis

description: Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model’s observed variables. The weight matrix may be calculated from the factor model’s parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors. Analytically, RCA equates to modeling with “regression method” factor scores, except that, while those scores will be inconsistent with the original factor model, they are strictly consistent with the RCA model. When the original factor model is strictly correct in the population and the composites in RCA are standardized, RCA parameter estimates replicate those from regression-weighted forms of partial least squares (PLS) path modeling and generalized structured component analysis (GSCA)—affirming that those methods also equate to modeling with regression method factor scores under the same conditions. Parallel measurement allows RCA to replicate both correlation weight and regression weight versions of PLS and GSCA. These results suggest that RCA and regression-weighted forms of PLS and GSCA are all consistent approaches for modeling data that conforms to a factor model. All analytical methods are described using one consistent symbol palette. Complete R syntax is provided.
DOI: 10.6084/m9.figshare.25782551
title: Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels

description: In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity’s consequences in multilevel models is clarified in novel ways.
DOI: 10.6084/m9.figshare.25782551.v1
title: Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels

description: In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity’s consequences in multilevel models is clarified in novel ways.
DOI: 10.6084/m9.figshare.25567277
title: Considering the ‘With Whom’: Differences Between Event- and Signal-Contingent ESM Data of Person-Specific Social Interactions

description: Experience sampling studies often aim to capture social interactions. A central methodological question in such studies is whether to use event- or signal-contingent sampling. The little existing research on this issue has not taken into account that social interactions occur with unique interaction partners (e.g., Anna or Tom). We analyze one week of social interaction data of 286 students from the University of Pittsburgh (60.8% male, mean age 19.2 years), taking into account the unique interaction partners of each student. Specifically, we investigate the differences between event- and signal contingent sampling in (1) the total number of unique interaction partners captured, as well as (2) the kinds of relationships, and (3) the quality of social interactions with these captured interaction partners. Apart from a larger quantity of interactions and unique interaction partners in the event-contingent sampling design, our analyses indicate subtle differences between the two designs when aiming to assess social interactions with more distant interaction partners, such as coworkers or strangers. Most importantly, in our analyses, specific interaction partners and social roles explained a considerable amount of variance in the quality of social interactions (up to 20.5%), suggesting that future research would benefit greatly from considering “with whom” individuals interact.
DOI: 10.6084/m9.figshare.25801626
title: Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections

description: The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.
DOI: 10.6084/m9.figshare.25801626.v1
title: Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections

description: The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.
DOI: 10.6084/m9.figshare.25801638
title: Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity

description: The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.
DOI: 10.6084/m9.figshare.25801638.v1
title: Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity

description: The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.
DOI: 10.6084/m9.figshare.25567277.v1
title: Considering the ‘With Whom’: Differences Between Event- and Signal-Contingent ESM Data of Person-Specific Social Interactions

description: Experience sampling studies often aim to capture social interactions. A central methodological question in such studies is whether to use event- or signal-contingent sampling. The little existing research on this issue has not taken into account that social interactions occur with unique interaction partners (e.g., Anna or Tom). We analyze one week of social interaction data of 286 students from the University of Pittsburgh (60.8% male, mean age 19.2 years), taking into account the unique interaction partners of each student. Specifically, we investigate the differences between event- and signal contingent sampling in (1) the total number of unique interaction partners captured, as well as (2) the kinds of relationships, and (3) the quality of social interactions with these captured interaction partners. Apart from a larger quantity of interactions and unique interaction partners in the event-contingent sampling design, our analyses indicate subtle differences between the two designs when aiming to assess social interactions with more distant interaction partners, such as coworkers or strangers. Most importantly, in our analyses, specific interaction partners and social roles explained a considerable amount of variance in the quality of social interactions (up to 20.5%), suggesting that future research would benefit greatly from considering “with whom” individuals interact.
DOI: 10.15468/dl.q3d569
title: Occurrence Download

description: A dataset containing 126967 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Torminalis glaberrima (Gand.) Sennikov & Kurtto)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 126967 records from 1235 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040688-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.