cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.9nnp7v
title: Occurrence Download

description: A dataset containing 11519 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.01078 -38.9182,-31.44871 -38.9182,-31.44871 14.03087,-80.01078 14.03087,-80.01078 -38.9182))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tapirus terrestris (Linnaeus, 1758)"
 ]
}

 The dataset includes 11519 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078039-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h82vub
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Chaoborus americanus (Johannsen, 1903)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 49 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078040-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6pbm8k
title: Occurrence Download

description: A dataset containing 1156 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Chaoborus crystallinus (De Geer, 1776)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 1156 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078041-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9225933
title: Daily Public Opinion on Israel-Palestine War
DOI: 10.6073/pasta/c021a2ebf1f91adf0ba3b5e53189c84f
title: Meteorological data from benchmark stations at the Andrews Experimental Forest, 1957 to present

description: A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations.
DOI: 10.5281/zenodo.13361940
title: Intra- and Inter-Format Variability of Psychophysiological and Technical Responses in Different Small-Sided Soccer Game Formats: A Repeated Measures Study
DOI: 10.5281/zenodo.13361941
title: Intra- and Inter-Format Variability of Psychophysiological and Technical Responses in Different Small-Sided Soccer Game Formats: A Repeated Measures Study
DOI: 10.15468/dl.gquc37
title: Occurrence Download

description: A dataset containing 1693 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Desmodesmus communis (E.Hegewald) E.Hegewald"
 ]
}

 The dataset includes 1693 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078042-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ka54xg
title: Occurrence Download

description: A dataset containing 11519 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-80.01078 -38.9182,-31.44871 -38.9182,-31.44871 14.03087,-80.01078 14.03087,-80.01078 -38.9182))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Tapirus terrestris (Linnaeus, 1758)"
 ]
}

 The dataset includes 11519 records from 95 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078043-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x4n9y2
title: Occurrence Download

description: A dataset containing 8712 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "SpeciesKey is Locusta migratoria (Linnaeus, 1758)",
 "Year is greater than or equal to 2004",
 "Year is less than or equal to 2023"
 ]
}

 The dataset includes 8712 records from 387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078044-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24167962
title: Ultimate Pólya Gamma Samplers–Efficient MCMC for Possibly Imbalanced Binary and Categorical Data

description: Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Pólya-Gamma random variables for a range of commonly encountered logistic regression models. From these latent variable representations, new Gibbs sampling algorithms for binary, binomial, and multinomial logit models are derived. All models allow for a conditionally Gaussian likelihood representation, rendering extensions to more complex modeling frameworks such as state space models straightforward. However, sampling efficiency may still be an issue in these data augmentation based estimation frameworks. To counteract this, novel marginal data augmentation strategies are developed and discussed in detail. The merits of our approach are illustrated through extensive simulations and real data applications. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24167962.v2
title: Ultimate Pólya Gamma Samplers–Efficient MCMC for Possibly Imbalanced Binary and Categorical Data

description: Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Pólya-Gamma random variables for a range of commonly encountered logistic regression models. From these latent variable representations, new Gibbs sampling algorithms for binary, binomial, and multinomial logit models are derived. All models allow for a conditionally Gaussian likelihood representation, rendering extensions to more complex modeling frameworks such as state space models straightforward. However, sampling efficiency may still be an issue in these data augmentation based estimation frameworks. To counteract this, novel marginal data augmentation strategies are developed and discussed in detail. The merits of our approach are illustrated through extensive simulations and real data applications. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24167962.v1
title: Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data

description: Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Pólya-Gamma random variables for a range of commonly encountered logistic regression models. From these latent variable representations, new Gibbs sampling algorithms for binary, binomial, and multinomial logit models are derived. All models allow for a conditionally Gaussian likelihood representation, rendering extensions to more complex modeling frameworks such as state space models straightforward. However, sampling efficiency may still be an issue in these data augmentation based estimation frameworks. To counteract this, novel marginal data augmentation strategies are developed and discussed in detail. The merits of our approach are illustrated through extensive simulations and real data applications.
DOI: 10.6084/m9.figshare.24147248
title: Smoothness-Penalized Deconvolution (SPeD) of a Density Estimate

description: This article addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared <i>m</i>th derivative. Theory for deconvolution has mainly focused on kernel- or wavelet-based techniques, but other methods including spline-based techniques and this smoothness-penalized estimator have been found to outperform kernel methods in simulation studies. This article fills in some of these gaps by establishing asymptotic guarantees for the smoothness-penalized approach. Consistency is established in mean integrated squared error, and rates of convergence are derived for Gaussian, Cauchy, and Laplace error densities, attaining some lower bounds already in the literature. The assumptions are weak for most results; the estimator can be used with a broader class of error densities than the deconvoluting kernel. Our application example estimates the density of the mean cytotoxicity of certain bacterial isolates under random sampling; this mean cytotoxicity can only be measured experimentally with additive error, leading to the deconvolution problem. We also describe a method for approximating the solution by a cubic spline, which reduces to a quadratic program. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24147248.v1
title: Smoothness-Penalized Deconvolution (SPeD) of a Density Estimate

description: This article addresses the deconvolution problem of estimating a square-integrable probability density from observations contaminated with additive measurement errors having a known density. The estimator begins with a density estimate of the contaminated observations and minimizes a reconstruction error penalized by an integrated squared <i>m</i>th derivative. Theory for deconvolution has mainly focused on kernel- or wavelet-based techniques, but other methods including spline-based techniques and this smoothness-penalized estimator have been found to outperform kernel methods in simulation studies. This article fills in some of these gaps by establishing asymptotic guarantees for the smoothness-penalized approach. Consistency is established in mean integrated squared error, and rates of convergence are derived for Gaussian, Cauchy, and Laplace error densities, attaining some lower bounds already in the literature. The assumptions are weak for most results; the estimator can be used with a broader class of error densities than the deconvoluting kernel. Our application example estimates the density of the mean cytotoxicity of certain bacterial isolates under random sampling; this mean cytotoxicity can only be measured experimentally with additive error, leading to the deconvolution problem. We also describe a method for approximating the solution by a cubic spline, which reduces to a quadratic program. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24545761
title: Sick Leave Data Paper.xlsx

description: This study presents the application of the item response theory (IRT) and classification algorithms to predict long-term sick leave in the unique environment of a public university. Sick leaves of 30 or more consecutive days are predicted based on job satisfaction scores obtained from an instrument developed <i>ad hoc</i> by using IRT and several individual characteristics. The predictive capability of four different machine learning algorithms was investigated, with the Random Forest, without oversampling, demonstrating superior performance in terms of accuracy (0.93) while the Naive Bayes model, when integrated with the ADASYN oversampling technique, emerged as the most effective predictor of sick leave, based on the confusion matrix and 0.80 accuracy score. These findings validate the potential of both models, establishing a promising foundation for further research. Furthermore, this innovative study illustrates the value of machine learning and IRT in identifying the propensity for long-term sick leave, and their potential as practical tools to aid Human Resources management. By enhancing such predictive insights, academic institutions can take proactive measures to manage and mitigate the impact of absenteeism, fostering a more productive and harmonious work environment.
DOI: 10.6084/m9.figshare.24545761.v1
title: Sick Leave Data Paper.xlsx

description: This study presents the application of the item response theory (IRT) and classification algorithms to predict long-term sick leave in the unique environment of a public university. Sick leaves of 30 or more consecutive days are predicted based on job satisfaction scores obtained from an instrument developed <i>ad hoc</i> by using IRT and several individual characteristics. The predictive capability of four different machine learning algorithms was investigated, with the Random Forest, without oversampling, demonstrating superior performance in terms of accuracy (0.93) while the Naive Bayes model, when integrated with the ADASYN oversampling technique, emerged as the most effective predictor of sick leave, based on the confusion matrix and 0.80 accuracy score. These findings validate the potential of both models, establishing a promising foundation for further research. Furthermore, this innovative study illustrates the value of machine learning and IRT in identifying the propensity for long-term sick leave, and their potential as practical tools to aid Human Resources management. By enhancing such predictive insights, academic institutions can take proactive measures to manage and mitigate the impact of absenteeism, fostering a more productive and harmonious work environment.
DOI: 10.6084/m9.figshare.24545794
title: Satis.dados.csv

description: A job satisfaction survey developed by a Brazilian federal university's sustainability committee containing 58 items across physical, cognitive, and organizational domains was longitudinally tested with 1,214 responses collected. The data underwent rigorous Item Response Theory (IRT) analysis, using the Graded Response Model, with tools such as frequency analysis, item characteristic curve, and full-information factor analysis in <i>RStudio</i>.
DOI: 10.5281/zenodo.10095594
title: Oferta de Automóviles de Segunda Mano en Cataluña

description: Oferta de Automóviles de Segunda Mano en Cataluña publicados en el portal web de Flexicar, www.flexicar.es, el día 7 de octubre de 2023
DOI: 10.5281/zenodo.10095593
title: Oferta de Automóviles de Segunda Mano en Cataluña

description: Oferta de Automóviles de Segunda Mano en Cataluña publicados en el portal web de Flexicar, www.flexicar.es, el día 7 de octubre de 2023
DOI: 10.5281/zenodo.10045264
title: VessMAP - Feature-Mapped Cortex Vasculature Dataset

description: VessMAP Dataset
The VessMAP Dataset comprises 100 manually labeled confocal microscopy images of mouse brain vasculature. Among these images, 20 randomly selected samples were labeled by two annotators. The dataset is organized using the following directory structure:

images: 100 blood vessel images (.tiff). Each image has a size of 256x256 pixels with a single grayscale channel of 8-bit color depth.
annotator1:

labels: 100 labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels annotated as blood vessels and a value of 0 assigned to background pixels.
skeletons: skeletons of the labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels belonging to the skeleton and a value of 0 assigned to background pixels. These skeletons were calculated using the Palàgyi-Kuba algorithm [1].
measures.json: a .json file with four metrics that can be used to map the dataset to a feature space. The four metrics included in the file are the contrast, blood vessel density, estimation of Gaussian noise level, and skeleton heterogeneity. A detailed explanation of the metrics can be found in [2]. These metrics are useful for testing possible biases when training supervised segmentation algorithms.
annotator2:

This directory contains annotations for 20 images that were randomly selected from the 100 images of the whole dataset. The organization is the same as for annotator 1.
1. K. Palágyi and A. Kuba, "A 3D 6-subiteration thinning algorithm for extracting medial lines," Pattern Recognit. Lett. 19, 613–627 (1998).
2. da Silva, MV., Santos, NdC., Lacoste, B., & Comin, CH. A new sampling methodology for creating rich, heterogeneous, subsets of samples for training image segmentation algorithms. arXiv preprint arXiv:2301.04517, (2023).
DOI: 10.5281/zenodo.10045265
title: VessMAP - Feature-Mapped Cortex Vasculature Dataset

description: VessMAP Dataset
The VessMAP Dataset comprises 100 manually labeled confocal microscopy images of mouse brain vasculature. Among these images, 20 randomly selected samples were labeled by two annotators. The dataset is organized using the following directory structure:

images: 100 blood vessel images (.tiff). Each image has a size of 256x256 pixels with a single grayscale channel of 8-bit color depth.
annotator1:

labels: 100 labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels annotated as blood vessels and a value of 0 assigned to background pixels.
skeletons: skeletons of the labels annotated by annotator 1. Each file is a 256x256 .png binary image, with a value of 255 assigned to pixels belonging to the skeleton and a value of 0 assigned to background pixels. These skeletons were calculated using the Palàgyi-Kuba algorithm [1].
measures.json: a .json file with four metrics that can be used to map the dataset to a feature space. The four metrics included in the file are the contrast, blood vessel density, estimation of Gaussian noise level, and skeleton heterogeneity. A detailed explanation of the metrics can be found in [2]. These metrics are useful for testing possible biases when training supervised segmentation algorithms.
annotator2:

This directory contains annotations for 20 images that were randomly selected from the 100 images of the whole dataset. The organization is the same as for annotator 1.
1. K. Palágyi and A. Kuba, "A 3D 6-subiteration thinning algorithm for extracting medial lines," Pattern Recognit. Lett. 19, 613–627 (1998).
2. da Silva, MV., Santos, NdC., Lacoste, B., & Comin, CH. A new sampling methodology for creating rich, heterogeneous, subsets of samples for training image segmentation algorithms. arXiv preprint arXiv:2301.04517, (2023).
DOI: 10.5281/zenodo.8125325
title: A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa

description: This dataset describes community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 178 household interviews conducted between August and September 2022, using mobile phones via KoboToolbox. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. It could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars
DOI: 10.5281/zenodo.8142947
title: A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa

description: This dataset describes community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 178 household interviews conducted between August and September 2022, using mobile phones via KoboToolbox. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. It could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars