cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.mrwc2x
title: Occurrence Download

description: A dataset containing 188871 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leuciscus leuciscus"
 ]
}

 The dataset includes 188871 records from 280 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003555-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17605/osf.io/dj3ct
title: Dataset for the paper "A Holistic AI-based Predictive Sales Analytics Process (HAiPSA‑Pro) for B2B Sales Forecasting"

description: Dataset used in a study presented in the paper "A Holistic AI-based Predictive Sales Analytics Process (HAiPSA‑Pro) for B2B Sales Forecasting".
DOI: 10.5061/dryad.5dv41nsc1
title: Episodic evolution of a protracted convergent margin revealed by detrital zircon geochronology in the Greater Caucasus

description: Convergent margins play a fundamental role in the construction and
 modification of Earth’s lithosphere and are characterized by poorly
 understood episodic processes that occur during the progression from
 subduction to terminal collision. On the northern margin of the active
 Arabia-Eurasia collision zone, the Greater Caucasus Mountains provide an
 opportunity to study a protracted convergent margin that spanned most of
 the Phanerozoic and culminated in Cenozoic continental collision. However,
 the main episodes of lithosphere formation and deformation along this
 margin remain enigmatic. Here, we use detrital zircon U-Pb geochronology
 from Paleozoic and Mesozoic (meta)sedimentary rocks in the Greater
 Caucasus, along with select zircon U-Pb and Hf isotopic data from coeval
 igneous rocks, to link key magmatic and depositional episodes along the
 Caucasus convergent margin. Devonian to Early Carboniferous rocks were
 deposited prior to Late Carboniferous accretion of the Greater Caucasus
 crystalline core onto the Laurussian margin. Permian to Triassic rocks
 document a period of northward subduction and forearc deposition south of
 a continental margin volcanic arc in the Northern Caucasus and Scythian
 Platform. Jurassic rocks record the opening of the Caucasus Basin as a
 back-arc rift during southward migration of the arc front into the Lesser
 Caucasus. Cretaceous rocks have few Jurassic-Cretaceous zircons,
 indicating a period of relative magmatic quiescence and minimal exhumation
 within this basin. Late Cenozoic closure of the Caucasus Basin juxtaposed
 the Lesser Caucasus arc to the south against the crystalline core of the
 Greater Caucasus to the north and led to the formation of a hypothesized
 terminal suture. We expect this suture to be within ~20 kilometers of the
 southern range front of the Greater Caucasus because all analyzed rocks to
 the north exhibit a provenance affinity with the crystalline core of the
 Greater Caucasus.
description: This dataset contains the Supporting Information for the
 manuscript entitled "Episodic evolution of a protracted convergent
 margin revealed by detrital zircon geochronology in the Greater
 Caucasus."  A description of the contents of the
 dataset can be found in the main Word document:<br>   
 Vasey_CaucasusDZ_SuppTextCaptions.docx
DOI: 10.57745/rygduw
title: Plant species distribution - Finland

description: High-resolution mapping of plant species using the GeoPl@ntNet tool: https://geo.plantnet.org/eco2adapt/ This interactive tool allows the exploration of distributions of species and biodiversity indicators produced by training a DeepSDM model, with predictions at a resolution of 50m per pixel
DOI: 10.5281/zenodo.18404538
title: Rate constants for 189 micropollutants in riverine biofilms
DOI: 10.5281/zenodo.18404537
title: Rate constants for 189 micropollutants in riverine biofilms
DOI: 10.5061/dryad.4qrfj6qgc
title: Mark-recapture dataset to estimate age-specific reproductive rates of Steller sea lions in Southeast Alaska

description: Age, region, and year-specific estimates of reproduction are needed for
 monitoring wildlife populations during periods of ecosystem
 change.  Population dynamics of Steller sea lions (Eumetopias
 jubatus) in Southeast Alaska varied regionally (with high population
 growth and survival in the north versus the south) and annually (with
 reduced adult female survival observed following a severe marine heatwave
 event), but reproductive performance is currently unknown.  We
 used mark-resighting data from 1,006 SSL females marked as pups at age ~3
 weeks of age from 1994–1995 and 2001–2005 and resighted from 2002–2019 (to
 a maximum age of 25) to examine age-, region-, and year-specific
 reproduction.  In the north versus the south, age of first
 reproduction was earlier (beginning at age 4 versus age 5, respectively)
 but annual birth probabilities of parous females were reduced by
 0.05.  In an average year pre-heatwave, the proportion of females
 with pup at the end of the pupping season peaked at ages 12–13 with
 ~0.60/0.65 (north/south) with pup, ~0.30/0.25 with juvenile, and ~0.10
 (both regions) without a dependent.  In both regions,
 reproductive senescence was gradual after age 12: ~0.40, 0.40, and 0.20 of
 females were in these reproductive states, respectively, by age
 20.  Correcting for neonatal mortality, true birth probabilities
 at peak ages were 0.66/0.72 (north/south).  No cost of
 reproduction on female survival was detected, but pup production remained
 lower (-0.06) after the heatwave event, which if sustained would result in
 population decline in the south.  Reduced pup production and
 greater retention of juveniles during periods of poor prey conditions may
 be an important strategy for Steller sea lions in Southeast Alaska, where
 fine-tuning reproduction based on nutritional status may improve the
 lifetime probability of producing pups under good conditions in a variable
 and less productive environment.
DOI: 10.5061/dryad.v15dv422h
title: Data from: Diversifying large-scale participatory science: The efficacy of engagement through facilitator organizations

description: Large-scale, scientist-led, participatory science (citizen science)
 projects often engage primarily participants who are White, wealthy, and
 well-educated. Calls to diversify contributory projects are increasingly
 common, but little research has evaluated the efficacy of suggested
 strategies for diversification. We engaged participants in Crowd the Tap
 through facilitator organizations like Historically Black Colleges and
 Universities (HBCUs), predominantly White institutions (PWI), high school
 science classrooms, and corporate volunteer programs. Crowd the Tap is a
 contributory project focused on identifying and addressing lead
 contamination in household drinking water in the United States. We
 investigated how participant diversity with respect to race, ethnicity,
 and homeownership (a proxy for income) differed between participation
 facilitated through a partner organization and in unfacilitated
 participation in which participants came to the project independently. We
 were also interested in which facilitators were most effective at
 increasing participant diversity. White and wealthy participants were
 overrepresented in unfacilitated participation. Facilitation helped
 increase engagement of people of color, especially Black households, and
 lower-income households. High schools were particularly effective at
 engaging Hispanic or Latino participants and HBCUs were important for
 engaging Black households. Ultimately, our results suggest that engagement
 through facilitator organizations may be an effective means of engaging
 diverse participants in large-scale projects. Our results have important
 implications for the field of participatory science as we seek to identify
 evidence-based strategies for diversifying project participants. 
description: The data was collected through an IRB approved survey in which
 Crowd the Tap participants submitted data on the types of pipes they had,
 the age of their home, water aesthetics, and demographic information. As
 part of this process, participants also indicated if they came to the
 project through a partner organization (what we call facilitator
 organizations). We used information on reported types of facilitators,
 race and ethnicity, and homeownership (as a proxy for income) to
 investigate how diversity differed across facilitation.
DOI: 10.15468/dl.8krpcq
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-22.64723 31.24421,13.64723 31.24421,13.64723 45.75579,-22.64723 45.75579,-22.64723 31.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oestophora ebria"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003646-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w8wvby
title: Occurrence Download

description: A dataset containing 6309 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chamaecyparis thyoides"
 ]
}

 The dataset includes 6309 records from 252 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003649-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w77e89
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-23.64723 28.74421,12.64723 28.74421,12.64723 43.25579,-23.64723 43.25579,-23.64723 28.74421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Oestophora calpeana"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003648-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rvn3wx
title: Occurrence Download

description: A dataset containing 11022 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Fraxinus nigra"
 ]
}

 The dataset includes 11022 records from 203 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003650-260126135527185/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uhdbad
title: Occurrence Download

description: A dataset containing 12183 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Commelina benghalensis"
 ]
}

 The dataset includes 12183 records from 513 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003644-260126135527185/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.28734941
title: Can You See The Change? Change Point Detection Using Visual Inference

description: Detecting change points is crucial in analyzing time series data and single-subject designs. This study investigates factors influencing change point detection through visual perception by employing a visual inference experiment. Participants were tasked with selecting the scatterplot that appeared most different from the surrounding plots. The “different” plot was generated to have a shift in the vertical direction compared to its counterparts with no vertical shift. We used a factorial experiment with a balanced incomplete block design to assign factor combinations for participants to evaluate. Furthermore, we compared the performance of visual accuracy to conventional change point detection methods. Participants were found to identify higher shift magnitudes more accurately than lower shift magnitudes, consistent with conventional methods. Change point data with higher variances had lower identification rates. Direction and change point location effects impacted identifying change point scenarios with lower signal-to-noise ratios. Participants indicated varying reasons for selection across correct and incorrect data plot identifications. Additionally, confidence in selection was positively associated with identification accuracy for change plots with higher signal-to-noise ratios. These insights highlight the complexities of change point detection through visual inference and emphasize the multifaceted nature of human perception in identifying subtle changes within data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.28734941.v1
title: Can You See The Change? Visual Perception in Change Point Analysis

description: Detecting change points is crucial in analyzing time series data and single-subject designs. This study investigates factors influencing change point detection through visual perception by employing a visual inference experiment. Participants were tasked with selecting the scatter plot that appeared most different from the surrounding plots. The” different” plot was generated to have a shift in the vertical direction compared to its counterparts with no vertical shift. We used a factorial experiment with a balanced incomplete block design to assign factor combinations for participants to evaluate. Furthermore, we compared the performance of visual accuracy to conventional change point detection methods. Participants were found to identify higher shift magnitudes more accurately than lower shift magnitudes, consistent with conventional methods. Change point data with higher variances had lower identification rates. Direction and change point location effects impacted identifying change point scenarios with lower signal-to-noise ratios. Participants indicated varying reasons for selection across correct and incorrect data plot identifications. Additionally, confidence in selection was positively associated with identification accuracy for change plots with higher signal-to-noise ratios. These insights highlight the complexities of change point detection through visual inference and emphasize the multifaceted nature of human perception in identifying subtle changes within data.
DOI: 10.6084/m9.figshare.28734941.v2
title: Can You See The Change? Change Point Detection Using Visual Inference

description: Detecting change points is crucial in analyzing time series data and single-subject designs. This study investigates factors influencing change point detection through visual perception by employing a visual inference experiment. Participants were tasked with selecting the scatterplot that appeared most different from the surrounding plots. The “different” plot was generated to have a shift in the vertical direction compared to its counterparts with no vertical shift. We used a factorial experiment with a balanced incomplete block design to assign factor combinations for participants to evaluate. Furthermore, we compared the performance of visual accuracy to conventional change point detection methods. Participants were found to identify higher shift magnitudes more accurately than lower shift magnitudes, consistent with conventional methods. Change point data with higher variances had lower identification rates. Direction and change point location effects impacted identifying change point scenarios with lower signal-to-noise ratios. Participants indicated varying reasons for selection across correct and incorrect data plot identifications. Additionally, confidence in selection was positively associated with identification accuracy for change plots with higher signal-to-noise ratios. These insights highlight the complexities of change point detection through visual inference and emphasize the multifaceted nature of human perception in identifying subtle changes within data. Supplementary materials for this article are available online.
DOI: 10.15468/dl.m5krt7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Winteria telescopa Brauer, 1901",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.9648 10.8496,26.9648 10.8496,26.9648 1.8496,35.9648 1.8496,35.9648 10.8496))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010042-250525065834625/datasets/export for details.
DOI: 10.6084/m9.figshare.28765094
title: ProSpar-GP: Scalable Gaussian Process Modeling with Massive Nonstationary Datasets

description: Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has been notable work on scaling up these models for big data, existing methods typically rely on a stationary GP assumption for approximation, and can thus perform poorly when the underlying response surface is nonstationary, that is, it has some regions of rapid change and other regions with little change. Such non-stationarity is, however, ubiquitous in real-world problems, including our motivating application for surrogate modeling of computer experiments. We propose a new Product of Sparse GP (ProSpar-GP) method for scalable GP modeling with massive nonstationary data. The ProSpar-GP makes use of a carefully-constructed product-of-experts formulation of sparse GP experts, where different experts are placed within local regions of non-stationarity. These GP experts are fit via a novel variational inference approach, which capitalizes on mini-batching and GPU acceleration for efficient optimization of inducing points and length-scale parameters for each expert. We further show that the ProSpar-GP is Kolmogorov-consistent, in that its generative distribution defines a valid stochastic process over the prediction space; such a property provides essential stability for variational inference, particularly in the presence of non-stationarity. We then demonstrate the improved performance of the ProSpar-GP over the state-of-the-art, in a suite of numerical experiments and an application for surrogate modeling of a satellite drag simulator. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.28765094.v2
title: ProSpar-GP: Scalable Gaussian Process Modeling with Massive Nonstationary Datasets

description: Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has been notable work on scaling up these models for big data, existing methods typically rely on a stationary GP assumption for approximation, and can thus perform poorly when the underlying response surface is nonstationary, that is, it has some regions of rapid change and other regions with little change. Such non-stationarity is, however, ubiquitous in real-world problems, including our motivating application for surrogate modeling of computer experiments. We propose a new Product of Sparse GP (ProSpar-GP) method for scalable GP modeling with massive nonstationary data. The ProSpar-GP makes use of a carefully-constructed product-of-experts formulation of sparse GP experts, where different experts are placed within local regions of non-stationarity. These GP experts are fit via a novel variational inference approach, which capitalizes on mini-batching and GPU acceleration for efficient optimization of inducing points and length-scale parameters for each expert. We further show that the ProSpar-GP is Kolmogorov-consistent, in that its generative distribution defines a valid stochastic process over the prediction space; such a property provides essential stability for variational inference, particularly in the presence of non-stationarity. We then demonstrate the improved performance of the ProSpar-GP over the state-of-the-art, in a suite of numerical experiments and an application for surrogate modeling of a satellite drag simulator. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.28765094.v1
title: ProSpar-GP: scalable Gaussian process modeling with massive non-stationary datasets

description: Gaussian processes (GPs) are a popular class of Bayesian nonparametric models, but its training can be computationally burdensome for massive training datasets. While there has been notable work on scaling up these models for big data, existing methods typically rely on a stationary GP assumption for approximation, and can thus perform poorly when the underlying response surface is non-stationary, i.e., it has some regions of rapid change and other regions with little change. Such non-stationarity is, however, ubiquitous in real-world problems, including our motivating application for surrogate modeling of computer experiments. We propose a new Product of Sparse GP (ProSpar-GP) method for scalable GP modeling with massive non-stationary data. The ProSpar-GP makes use of a carefully-constructed product-of-experts formulation of sparse GP experts, where different experts are placed within local regions of non-stationarity. These GP experts are fit via a novel variational inference approach, which capitalizes on mini-batching and GPU acceleration for efficient optimization of inducing points and length-scale parameters for each expert. We further show that the ProSpar-GP is Kolmogorov-consistent, in that its generative distribution defines a valid stochastic process over the prediction space; such a property provides essential stability for variational inference, particularly in the presence of non-stationarity. We then demonstrate the improved performance of the ProSpar-GP over the state-of-the-art, in a suite of numerical experiments and an application for surrogate modeling of a satellite drag simulator. Supplemental materials for this article are available online.
DOI: 10.17632/pdsffmvky4
title: Authorship changes when publishing the works presented in national student forums, Cambios de autoría al publicar los trabajos presentados en fórums nacionales estudiantiles

description: Introduction: The problems with the articles' authorship are frequent; however, in Cuba there are few investigations on the topic.
Objective: To determine the authorship changes in the publication of works presented in medical student forums at national level in Cuba and its associated factors.
Methods: Observational and cross-sectional study using analytical techniques, that included all the works presented in 2016, 2017 and 2019 editions of the medical student forums at national level. A Google Scholar search strategy was carried out in order to check if these works had been published in scientific journals. After, authorship changes were identified and associated factors were looked for. Prevalence ratios (aPR), 95 % confidence intervals (95 % CI) and p values using generalized linear models were obtained.
Results: Of the 129 published works, 89,9 % (n= 116) had authorship changes. In the multivariate analysis, there was more frequency of authorship changes when the articles were published in professionals' journals (aPR: 1,29; IC 95 %: 1,11 - 1,50; p= 0,001) and when the publication occurred later to the event (aPR: 1,20; IC 95 %: 1,01 - 1,42; p= 0,042); that which was contrary when the corresponding author was a student (aPR: 0,79; IC 95 %: 0,66 - 0,95; p= 0,010), adjusted by three variables.
Conclusion: Almost the entirety of the articles had authorship changes and some factors associated to a higher or smaller frequency in those changes were determined (publication, time and corresponding author).