cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.23708/33f2115e-3162-4848-83d2-ba56f1002f5f
title: CARIBCOAST: Coastal ecosystems distribution and Ecosystem Based Coastal Vulnerability Index (EBCVI) in the Caribbean

description: The dataset is composed of raster (mangrove, seagrass and coral distribution - geotiff format) and vector (caostal vulnerability index CVI - shapefile format) layers for the six areas of interest of the project (Guadeloupe, Martinique, Saint-Martin, Jamaica, Puerto Rico and Trinidand and Tobago), including style files in qml format. For each area, files corresponding to selected pilot sites are available. The distribution maps of coastal ecosystems (mangrove, seagrass and coral) have been produced using Sentinel 2, Pleiades and Planetscope satellites images (provided by the ESA Copernicus program, the Dataterra DINAMIS platform and Planets) and a random forest supervised classification approach (carried out by La Telescop). The production of the EBCVI to erosion is based on the distribution maps of the ecosystems which were used to calculate a number of landscape metrics, that were combined to produce the EBCVI. Associated ressources provide different documents (in French and English) with more details on the methodology and some technical reports. 
More details :
https://www.carib-coast.com/en/
Balouin, Y., de la Torre, Y., Pedreros, R., Filippini, A. G., Laigre, T., Videau, N., ... & Jack, C. (2023). Carib-coast project: Caribbean network for prevention of coastal risks related to climate change. The Proceedings of the Coastal Sediments 2023 In 5 Volumes, https://doi.org/10.1142/9789811275135_0116.
DOI: 10.6084/m9.figshare.22769722
title: Genetic factors underlying insomnia and ovarian insufficiency

description: Premature ovarian insufficiency (POI) is characterized by a loss of regular hormone production and egg release in women below the age of 40 years, which often leads to infertility, vaginal dryness and dysfunctional sleep. Acknowledging the common co-occurrence of insomnia and POI, we tested the overlap between POI and insomnia-associated genes, which were implicated in previous large-scale populational genetics efforts. Among the 27 overlapping genes, three pathways were found as enriched: DNA replication, homologous recombination and Fanconi anemia. We then describe biological mechanisms, which link these pathways to a dysfunctional regulation and response to oxidative stress. We propose that oxidative stress may correspond to one of the convergent cellular processes between ovarian malfunction and insomnia pathogenic etiology. This overlap might also be driven by cortisol release associated with dysregulated DNA repair mechanisms. Benefiting from the enormous advances in populational genetics studies, this study provides a novel outlook on the relationship between insomnia and POI. The shared genetic factors and critical biological nodes between these two comorbidities may lead to identification of putative pharmacological and therapeutical targets, which can leverage novel approaches to treat or alleviate their symptoms.
DOI: 10.6084/m9.figshare.22769722.v1
title: Genetic factors underlying insomnia and ovarian insufficiency

description: Premature ovarian insufficiency (POI) is characterized by a loss of regular hormone production and egg release in women below the age of 40 years, which often leads to infertility, vaginal dryness and dysfunctional sleep. Acknowledging the common co-occurrence of insomnia and POI, we tested the overlap between POI and insomnia-associated genes, which were implicated in previous large-scale populational genetics efforts. Among the 27 overlapping genes, three pathways were found as enriched: DNA replication, homologous recombination and Fanconi anemia. We then describe biological mechanisms, which link these pathways to a dysfunctional regulation and response to oxidative stress. We propose that oxidative stress may correspond to one of the convergent cellular processes between ovarian malfunction and insomnia pathogenic etiology. This overlap might also be driven by cortisol release associated with dysregulated DNA repair mechanisms. Benefiting from the enormous advances in populational genetics studies, this study provides a novel outlook on the relationship between insomnia and POI. The shared genetic factors and critical biological nodes between these two comorbidities may lead to identification of putative pharmacological and therapeutical targets, which can leverage novel approaches to treat or alleviate their symptoms.
DOI: 10.6084/m9.figshare.26090915
title: Complete and Immediate Resolution of See-Saw Nystagmus Following Pituitary Macroadenoma Resection: Case Report and Review of the Literature

description: See-saw nystagmus (SSN) is a rare form of nystagmus characterised by alternating elevation with incyclotorsion of one eye and concomitant depression with excyclotorsion of the other eye, often due to abnormalities involving the midbrain and parasellar region. Herein, we highlight a rare case of pendular SSN, which demonstrated complete resolution following resection of a pituitary macroadenoma. A patient in their 40s was identified to have SSN and was diagnosed with a pituitary macroadenoma. They underwent an endoscopic endonasal transsellar approach for resection of the pituitary adenoma. Their nystagmus resolved immediately after surgery. From a review of the literature, resolution and/or significant improvement in SSN occurred in 74% of cases following treatment, with 100%, 86% and 50% following treatment for medication-induced, neurological infarcts, and mass-effect aetiologies of SSN, respectively. SSN is a rare entity with a wide array of aetiologies. Identification of the causative aetiology and appropriate treatment can lead to significant improvement or resolution of the nystagmus in most cases.
DOI: 10.6084/m9.figshare.26090915.v1
title: Complete and Immediate Resolution of See-Saw Nystagmus Following Pituitary Macroadenoma Resection: Case Report and Review of the Literature

description: See-saw nystagmus (SSN) is a rare form of nystagmus characterised by alternating elevation with incyclotorsion of one eye and concomitant depression with excyclotorsion of the other eye, often due to abnormalities involving the midbrain and parasellar region. Herein, we highlight a rare case of pendular SSN, which demonstrated complete resolution following resection of a pituitary macroadenoma. A patient in their 40s was identified to have SSN and was diagnosed with a pituitary macroadenoma. They underwent an endoscopic endonasal transsellar approach for resection of the pituitary adenoma. Their nystagmus resolved immediately after surgery. From a review of the literature, resolution and/or significant improvement in SSN occurred in 74% of cases following treatment, with 100%, 86% and 50% following treatment for medication-induced, neurological infarcts, and mass-effect aetiologies of SSN, respectively. SSN is a rare entity with a wide array of aetiologies. Identification of the causative aetiology and appropriate treatment can lead to significant improvement or resolution of the nystagmus in most cases.
DOI: 10.6084/m9.figshare.27264296
title: A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores

description: Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.g., means). For this, we first provide the theoretical underpinnings of the mechanisms under study and then provide two focused simulations that (i) directly expand on the collider scenario in Thoemmes and Rose (2014, appendix A) by considering outcomes that are relevant to norming and (ii) extend the scenarios under consideration by instrumental variable mechanisms. We illustrate the bias mechanisms for two different norming approaches and exemplify the procedures by means of an empirical example. We end by discussing limitations and implications of our research.
DOI: 10.6084/m9.figshare.27264296.v1
title: A Causal View on Bias in Missing Data Imputation: The Impact of Evil Auxiliary Variables on Norming of Test Scores

description: Among the most important merits of modern missing data techniques such as multiple imputation (MI) and full-information maximum likelihood estimation is the possibility to include additional information about the missingness process via auxiliary variables. During the past decade, the choice of auxiliary variables has been investigated under a variety of different conditions and more recent research points to the potentially biasing effect of certain auxiliary variables, particularly colliders (Thoemmes & Rose, 2014). In this article, we further extend biasing mechanisms of certain auxiliary variables considered in previous research and thereby focus on their effects on individual diagnosis based on norming, in which the whole distribution of a variable is of interest rather than average coefficients (e.g., means). For this, we first provide the theoretical underpinnings of the mechanisms under study and then provide two focused simulations that (i) directly expand on the collider scenario in Thoemmes and Rose (2014, appendix A) by considering outcomes that are relevant to norming and (ii) extend the scenarios under consideration by instrumental variable mechanisms. We illustrate the bias mechanisms for two different norming approaches and exemplify the procedures by means of an empirical example. We end by discussing limitations and implications of our research.
DOI: 10.6084/m9.figshare.27242529
title: Make Some Noise: Generating Data from Imperfect Factor Models

description: Researchers simulating covariance structure models sometimes add model error to their data to produce model misfit. Presently, the most popular methods for generating error-perturbed data are those by Tucker, Koopman, and Linn (TKL), Cudeck and Browne (CB), and Wu and Browne (WB). Although all of these methods include parameters that control the degree of model misfit, none can generate data that reproduce multiple fit indices. To address this issue, we describe a multiple-target TKL method that can generate error-perturbed data that will reproduce target RMSEA and CFI values either individually or together. To evaluate this method, we simulated error-perturbed correlation matrices for an array of factor analysis models using the multiple-target TKL method, the CB method, and the WB method. Our results indicated that the multiple-target TKL method produced solutions with RMSEA and CFI values that were closer to their target values than those of the alternative methods. Thus, the multiple-target TKL method should be a useful tool for researchers who wish to generate error-perturbed correlation matrices with a known degree of model error. All functions that are described in this work are available in the fungible R library. Additional materials (e.g., R code, supplemental results) are available at https://osf.io/vxr8d/.
DOI: 10.6084/m9.figshare.27242529.v1
title: Make Some Noise: Generating Data from Imperfect Factor Models

description: Researchers simulating covariance structure models sometimes add model error to their data to produce model misfit. Presently, the most popular methods for generating error-perturbed data are those by Tucker, Koopman, and Linn (TKL), Cudeck and Browne (CB), and Wu and Browne (WB). Although all of these methods include parameters that control the degree of model misfit, none can generate data that reproduce multiple fit indices. To address this issue, we describe a multiple-target TKL method that can generate error-perturbed data that will reproduce target RMSEA and CFI values either individually or together. To evaluate this method, we simulated error-perturbed correlation matrices for an array of factor analysis models using the multiple-target TKL method, the CB method, and the WB method. Our results indicated that the multiple-target TKL method produced solutions with RMSEA and CFI values that were closer to their target values than those of the alternative methods. Thus, the multiple-target TKL method should be a useful tool for researchers who wish to generate error-perturbed correlation matrices with a known degree of model error. All functions that are described in this work are available in the fungible R library. Additional materials (e.g., R code, supplemental results) are available at https://osf.io/vxr8d/.
DOI: 10.6084/m9.figshare.28081609
title: A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series

description: Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and non-stationary trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.
DOI: 10.6084/m9.figshare.28081609.v1
title: A Gentle Introduction and Application of Feature-Based Clustering with Psychological Time Series

description: Psychological researchers and practitioners collect increasingly complex time series data aimed at identifying differences between the developments of participants or patients. Past research has proposed a number of dynamic measures that describe meaningful developmental patterns for psychological data (e.g., instability, inertia, linear trend). Yet, commonly used clustering approaches are often not able to include these meaningful measures (e.g., due to model assumptions). We propose feature-based time series clustering as a flexible, transparent, and well-grounded approach that clusters participants based on the dynamic measures directly using common clustering algorithms. We introduce the approach and illustrate the utility of the method with real-world empirical data that highlight common ESM challenges of multivariate conceptualizations, structural missingness, and non-stationary trends. We use the data to showcase the main steps of input selection, feature extraction, feature reduction, feature clustering, and cluster evaluation. We also provide practical algorithm overviews and readily available code for data preparation, analysis, and interpretation.
DOI: 10.5281/zenodo.18325302
title: Evaluating vegetation restoration following a transition from sheep to cattle grazing at a calcareous upland site.

description: 1.       Much of the UK uplands are dominated by species-poor grasslands, largely a result of intensive sheep grazing. In some areas, efforts are underway to restore more natural habitats by reducing the number of sheep, with possible benefits to biodiversity, flood alleviation and carbon storage. Restoration aims to improve vegetation diversity, reduce the dominance of grasses and increase vegetation height and structural complexity.

2.       This study evaluates whether changing grazing from light sheep grazing to light cattle grazing has restored vegetation at an upland calcareous site in the Yorkshire Dales National Park in England. Vegetation surveys were conducted over 19 quadrats of 2 m x 2 m, surveyed every four years over a 12-year period. Generalised Linear Mixed Models were used to understand how a number of key vegetation characteristics, relevant to restoration success, have changed with increasing years since sheep removal.

3.       Changing from sheep to cattle grazing led to a change in vegetation species composition. Species richness, calcareous grassland indicators and proportion cover of bryophytes were found to increase with increasing years since sheep removal. In contrast, vegetation height and the proportion cover of grass decreased.

4.       Our results show that switching from sheep to cattle grazing was highly successful in restoring diversity and habitat quality in calcareous landscapes. The less selective grazing of cattle led to a more open, less grass dominated sward, allowing calcareous grassland wildflowers such as Linum catharticum and Lotus corniculatus to increase in frequency. However, the vegetation became shorter, another important objective for vegetation restoration in the uplands, suggesting a possible trade-off between diversity and vegetation height at a small scale.

Practical implication: Changing from sheep grazing, even at a low intensity, to extensive cattle grazing restores calcareous habitat diversity and quality. Restoration of vegetation height and structure may require other grazing management such as livestock exclusion.
DOI: 10.6084/m9.figshare.27325436
title: Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary

description: Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes. We provide a formula that decomposes the difference-in-coefficients estimate into (1) an estimate of non-collapsibility, and (2) an indirect effect estimate. We use a simulation study and an empirical data example to illustrate the impact of non-collapsibility on the difference-in-coefficients estimate of the indirect effect. Further, we demonstrate the application of several alternative methods for estimating the indirect effect, including the product-of-coefficients method and regression-based causal mediation analysis. The results emphasize the importance of choosing a method for estimating the indirect effect that is not affected by non-collapsibility.
DOI: 10.6084/m9.figshare.27325436.v1
title: Why You Should Not Estimate Mediated Effects Using the Difference-in-Coefficients Method When the Outcome is Binary

description: Despite previous warnings against the use of the difference-in-coefficients method for estimating the indirect effect when the outcome in the mediation model is binary, the difference-in-coefficients method remains readily used in a variety of fields. The continued use of this method is presumably because of the lack of awareness that this method conflates the indirect effect estimate and non-collapsibility. In this paper, we aim to demonstrate the problems associated with the difference-in-coefficients method for estimating indirect effects for mediation models with binary outcomes. We provide a formula that decomposes the difference-in-coefficients estimate into (1) an estimate of non-collapsibility, and (2) an indirect effect estimate. We use a simulation study and an empirical data example to illustrate the impact of non-collapsibility on the difference-in-coefficients estimate of the indirect effect. Further, we demonstrate the application of several alternative methods for estimating the indirect effect, including the product-of-coefficients method and regression-based causal mediation analysis. The results emphasize the importance of choosing a method for estimating the indirect effect that is not affected by non-collapsibility.
DOI: 10.6084/m9.figshare.27992545
title: Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters

description: The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying a unidimensional IRT model to multidimensional data causes violations of local independence, which can vitiate IRT applications. To evaluate and, possibly, remedy the problems caused by forcing unidimensional models onto multidimensional data, we consider the creation of a projected unidimensional IRT model, where the multidimensionality caused by nuisance dimensions is controlled for by integrating them out from the model. Specifically, when item response data have a bifactor structure, one can create a unidimensional model based on projecting to the general factor. Importantly, the projected unidimensional IRT model can be used as a benchmark for comparison to a unidimensional model to judge the practical consequences of multidimensionality. Limitations of the proposed approach are detailed.
DOI: 10.6084/m9.figshare.27992545.v1
title: Using Projective IRT to Evaluate the Effects of Multidimensionality on Unidimensional IRT Model Parameters

description: The application of unidimensional IRT models requires item response data to be unidimensional. Often, however, item response data contain a dominant dimension, as well as one or more nuisance dimensions caused by content clusters. Applying a unidimensional IRT model to multidimensional data causes violations of local independence, which can vitiate IRT applications. To evaluate and, possibly, remedy the problems caused by forcing unidimensional models onto multidimensional data, we consider the creation of a projected unidimensional IRT model, where the multidimensionality caused by nuisance dimensions is controlled for by integrating them out from the model. Specifically, when item response data have a bifactor structure, one can create a unidimensional model based on projecting to the general factor. Importantly, the projected unidimensional IRT model can be used as a benchmark for comparison to a unidimensional model to judge the practical consequences of multidimensionality. Limitations of the proposed approach are detailed.