cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25403/upresearchdata.27527067
title: Variables influencing warthog occupancy, focusing on the presence of aardvark (Orycteropus afer) as a possible keystone supporting burrowing species

description: The dataset underpinned a study titled "Factors affecting warthog (Phacochoerus africanus) occupancy with presence of aardvark (Orycteropus afer) as a keystone species". The study investigated the dynamics of warthog (<em>Phacochoerus africanus</em>)<em> </em>occupancy across two diverse properties. Warthog are a common mammal species prevalent across diverse landscapes in Africa. Moreover, warthog are carriers of African swine fever, posing a threat to swine biosecurity. The primary objective of the study was to determine the variables influencing warthog occupancy, focusing on the presence of aardvark (<em>Orycteropus afer</em>) as a possible keystone due to its facilitative capabilities supporting burrowing species. The findings provide a foundation for future studies aiming to map potential warthog distribution based on influential covariates. Hypothesis considered a range of factors such as the presence of aardvark, the presence of predators, vegetation type, soil composition, distance to perennial water sources, proximity to roads, distance to the nearest 10 trees (tree cover at site level), and camera efficiency, was tested using occupancy modelling with camera trap data from Atherstone Private Game Reserve and Mountain Zebra National Park. The results revealed that warthog occupancy is positively influenced by the presence of aardvark and camera efficiency, while it is negatively influenced by the presence of predators (lion,<em> Panthera leo</em> and leopard,<em> Panthera pardus</em>), increased distance to perennial water sources, and shorter average distance to the nearest ten trees (potentially linked to vegetation structure/type). These covariates also impacted the detection probability of warthog at both study sites, highlighting the complexity of the factors influencing their occupancy.
DOI: 10.25403/upresearchdata.27527067.v1
title: Variables influencing warthog occupancy, focusing on the presence of aardvark (Orycteropus afer) as a possible keystone supporting burrowing species

description: The dataset underpinned a study titled "Factors affecting warthog (Phacochoerus africanus) occupancy with presence of aardvark (Orycteropus afer) as a keystone species". The study investigated the dynamics of warthog (<em>Phacochoerus africanus</em>)<em> </em>occupancy across two diverse properties. Warthog are a common mammal species prevalent across diverse landscapes in Africa. Moreover, warthog are carriers of African swine fever, posing a threat to swine biosecurity. The primary objective of the study was to determine the variables influencing warthog occupancy, focusing on the presence of aardvark (<em>Orycteropus afer</em>) as a possible keystone due to its facilitative capabilities supporting burrowing species. The findings provide a foundation for future studies aiming to map potential warthog distribution based on influential covariates. Hypothesis considered a range of factors such as the presence of aardvark, the presence of predators, vegetation type, soil composition, distance to perennial water sources, proximity to roads, distance to the nearest 10 trees (tree cover at site level), and camera efficiency, was tested using occupancy modelling with camera trap data from Atherstone Private Game Reserve and Mountain Zebra National Park. The results revealed that warthog occupancy is positively influenced by the presence of aardvark and camera efficiency, while it is negatively influenced by the presence of predators (lion,<em> Panthera leo</em> and leopard,<em> Panthera pardus</em>), increased distance to perennial water sources, and shorter average distance to the nearest ten trees (potentially linked to vegetation structure/type). These covariates also impacted the detection probability of warthog at both study sites, highlighting the complexity of the factors influencing their occupancy.
DOI: 10.57745/adrwie
title: 600_2_NL_Bb_H_108_066.tab
DOI: 10.57745/5bbo84
title: 60_031.json
DOI: 10.13023/2024.alum.21
title: Grape Juice

description: Includes data on the aluminum content of  grape juice.
DOI: 10.57745/bsyk3q
title: 600_3_NL_Bb_H_108_066.tab
DOI: 10.57745/zuraew
title: 60_1_Ref_T_29_016.tab
DOI: 10.57745/hay9wx
title: 602_2_Cal_STD_108_066.tab
DOI: 10.57745/y8dlc9
title: 60_2_Ref_T_29_016.tab
DOI: 10.6084/m9.figshare.22549949.v2
title: A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance

description: This article discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including neural networks. We first show that a functional variance, a key concept in defining a widely-applicable information criterion, characterizes the generalization gap even in overparameterized settings where a conventional theory cannot be applied. As the computational cost of the functional variance is expensive for the overparameterized models, we propose an efficient approximation of the function variance, the Langevin approximation of the functional variance (Langevin FV). This method leverages only the first-order gradient of the squared loss function, without referencing the second-order gradient; this ensures that the computation is efficient and the implementation is consistent with gradient-based optimization algorithms. We demonstrate the Langevin FV numerically by estimating the generalization gaps of overparameterized linear regression and nonlinear neural network models, containing more than a thousand of parameters therein. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21817917
title: Maximum Likelihood Algorithm for Spatial Generalized Linear Mixed Models without Numerical Evaluations of Intractable Integrals

description: Spatial generalized linear mixed effects models are popular in spatial or spatiotemporal data analysis when the responses are counts and the random effects are modeled by multivariate normal distributions. Direct computation of the MLEs of model parameters is impossible because the likelihood functions contain high-dimensional intractable integrals. To overcome the difficulty, a new method called the prediction-maximization algorithm is proposed. The method has a maximization step for the MLEs of spatial linear mixed effects models for normal responses and a prediction step for the prediction of the random effects. None of them involves high-dimensional intractable integrals. Because only algorithms for the normal responses are needed, the derivation of the MLEs of a spatial generalized linear mixed effects model for count responses by the proposed method is not computationally harder than a model for normal responses. The simulation study shows that the performance of the proposed method is comparable to that of the previous maximum likelihood algorithms formulated by high-order Laplace approximations and is better than that of Bayesian methods formulated by MCMC algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21817917.v1
title: Maximum Likelihood Algorithm for Spatial Generalized Linear Mixed Models without Numerical Evaluations of Intractable Integrals

description: Spatial generalized linear mixed effects models are popular in spatial or spatiotemporal data analysis when the responses are counts and the random effects are modeled by multivariate normal distributions. Direct computation of the MLEs of model parameters is impossible because the likelihood functions contain high-dimensional intractable integrals. To overcome the difficulty, a new method called the prediction-maximization algorithm is proposed. The method has a maximization step for the MLEs of spatial linear mixed effects models for normal responses and a prediction step for the prediction of the random effects. None of them involves high-dimensional intractable integrals. Because only algorithms for the normal responses are needed, the derivation of the MLEs of a spatial generalized linear mixed effects model for count responses by the proposed method is not computationally harder than a model for normal responses. The simulation study shows that the performance of the proposed method is comparable to that of the previous maximum likelihood algorithms formulated by high-order Laplace approximations and is better than that of Bayesian methods formulated by MCMC algorithms. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.22549949
title: A Generalization Gap Estimation for Overparameterized Models via the Langevin Functional Variance

description: This article discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including neural networks. We first show that a functional variance, a key concept in defining a widely-applicable information criterion, characterizes the generalization gap even in overparameterized settings where a conventional theory cannot be applied. As the computational cost of the functional variance is expensive for the overparameterized models, we propose an efficient approximation of the function variance, the Langevin approximation of the functional variance (Langevin FV). This method leverages only the first-order gradient of the squared loss function, without referencing the second-order gradient; this ensures that the computation is efficient and the implementation is consistent with gradient-based optimization algorithms. We demonstrate the Langevin FV numerically by estimating the generalization gaps of overparameterized linear regression and nonlinear neural network models, containing more than a thousand of parameters therein. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.22549949.v1
title: A generalization gap estimation for overparameterized models via the Langevin functional variance

description: This paper discusses the estimation of the generalization gap, the difference between generalization performance and training performance, for overparameterized models including neural networks. We first show that a functional variance, a key concept in defining a widely-applicable information criterion, characterizes the generalization gap even in overparameterized settings where a conventional theory cannot be applied. As the computational cost of the functional variance is expensive for the overparameterized models, we propose an efficient approximation of the function variance, the Langevin approximation of the functional variance (Langevin FV). This method leverages only the 1st-order gradient of the squared loss function, without referencing the 2nd-order gradient; this ensures that the computation is efficient and the implementation is consistent with gradient-based optimization algorithms. We demonstrate the Langevin FV numerically by estimating the generalization gaps of overparameterized linear regression and non-linear neural network models, containing more than a thousand of parameters therein.
DOI: 10.6084/m9.figshare.21977640
title: Bayesian Nonparametric Modeling of Conditional Multidimensional Dependence Structures

description: In recent years, conditional copulas, that allow dependence between variables to vary according to the values of one or more covariates, have attracted increasing attention. However, the literature mainly focused on the bivariate case, since the constraints on the multivariate copulas correlation matrices would make the specifications of covariates arduous. In high dimension, vine copulas offer greater flexibility compared to multivariate copulas, since they are constructed using bivariate copulas as building blocks. We present a novel inferential approach for multivariate distributions, which combines the flexibility of vine constructions with the advantages of Bayesian nonparametrics, not requiring the specification of parametric families for each pair copula. Expressing multivariate copulas using vines allows us to easily account for covariate specifications driving the dependence between response variables. We specify the vine copula density as an infinite mixture of Gaussian copulas, defining a Dirichlet process prior on the mixing measure, and performing posterior inference via Markov chain Monte Carlo sampling. Our approach is successful as for clustering as well as for density estimation. We carry out simulation studies and apply the proposed approach to analyze a veterinary dataset and to investigate the impact of natural disasters on financial development. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.21977640.v1
title: Bayesian Nonparametric Modelling of Conditional Multidimensional Dependence Structures

description: In recent years, conditional copulas, that allow dependence between variables to vary according to the values of one or more covariates, have attracted increasing attention. However, the literature mainly focused on the bivariate case, since the constraints on the multivariate copulas correlation matrices would make the specifications of covariates arduous. In high dimension, vine copulas offer greater flexibility compared to multivariate copulas, since they are constructed using bivariate copulas as building blocks. We present a novel inferential approach for multivariate distributions, which combines the flexibility of vine constructions with the advantages of Bayesian nonparametrics, not requiring the specification of parametric families for each pair copula. Expressing multivariate copulas using vines allows us to easily account for covariate specifications driving the dependence between response variables. We specify the vine copula density as an infinite mixture of Gaussian copulas, defining a Dirichlet process prior on the mixing measure, and performing posterior inference via Markov chain Monte Carlo sampling. Our approach is successful as for clustering as well as for density estimation. We carry out simulation studies and apply the proposed approach to analyse a veterinary dataset and to investigate the impact of natural disasters on financial development. Supplementary materials are available online.
DOI: 10.57745/9acwwd
title: 603_1_Ref_V_109_067.tab
DOI: 10.57745/zpqy8z
title: 603_2_Ref_V_109_067.tab
DOI: 10.57745/uzmr5r
title: 603_3_Ref_V_109_067.tab
DOI: 10.57745/bntxl2
title: 60_3_Ref_T_29_016.tab
DOI: 10.57745/clatmj
title: 604_1_Ref_T_109_067.tab
DOI: 10.15468/dl.ur3g6k
title: Occurrence Download

description: A dataset containing 274 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Puya aequatorialis André"
 ]
}

 The dataset includes 274 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024540-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/i8zje1
title: 604_2_Ref_T_109_067.tab
DOI: 10.57745/ka2x5s
title: 604_3_Ref_T_109_067.tab
DOI: 10.57745/oakiz2
title: 605_1_L_Jr_iBCC_109_067.tab
DOI: 10.57745/hrcmjf
title: 605_2_L_Jr_iBCC_109_067.tab
DOI: 10.13023/2024.alum.22
title: Kiwi and Kiwi Juice

description: Includes data on the aluminum content of kiwi and kiwi juice.
DOI: 10.15468/dl.kmudxf
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sobralia uribei P.Ortiz"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024541-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/v6me0s
title: 605_3_L_Jr_iBCC_109_067.tab
DOI: 10.15468/dl.kx3meq
title: Occurrence Download

description: A dataset containing 2164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Specimen, Occurrence evidence)",
 "Continent is North America",
 "OccurrenceStatus is Present",
 "TaxonKey is Euphyes dion (Edwards, 1879)",
 "Year 2004-2024"
 ]
}

 The dataset includes 2164 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024542-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/ojpc8z
title: 608_1_NL_Jr_H_109_067.tab
DOI: 10.57745/qqcb1q
title: 608_2_NL_Jr_H_109_067.tab
DOI: 10.57745/fs2mpd
title: 608_3_NL_Jr_H_109_067.tab
DOI: 10.57745/idxsf8
title: 609_1_NL_Jr_H_109_067.tab
DOI: 10.57745/srsd0x
title: 609_2_NL_Jr_H_109_067.tab
DOI: 10.57745/ij4dve
title: 609_3_NL_Jr_H_109_067.tab
DOI: 10.57745/l1rswl
title: 610_1_L_Fl_iBCC_109_067.tab
DOI: 10.15468/dl.qeyvb2
title: Occurrence Download

description: A dataset containing 2123 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bryoxiphium norvegicum (Brid.) Mitt."
 ]
}

 The dataset includes 2123 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024544-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/mhlykg
title: 610_2_L_Fl_iBCC_109_067.tab
DOI: 10.57745/njfvhk
title: 61_032.json
DOI: 10.57745/rcjshk
title: 610_3_L_Fl_iBCC_109_067.tab
DOI: 10.57745/vhd17f
title: 61_1_L_Bl_isSCC_29_016.tab
DOI: 10.57745/dwbzss
title: 61_2_L_Bl_isSCC_29_016.tab
DOI: 10.57745/vhnt1e
title: 613_1_NL_Fl_H_109_067.tab
DOI: 10.57745/ns7jp7
title: 613_2_NL_Fl_H_109_067.tab
DOI: 10.57745/wwekyg
title: 613_3_NL_Fl_H_109_067.tab
DOI: 10.57745/qwcjd9
title: 61_3_L_Bl_isSCC_29_016.tab
DOI: 10.15468/dl.zcrpvu
title: Occurrence Download

description: A dataset containing 1127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Specimen, Occurrence evidence)",
 "Continent is North America",
 "OccurrenceStatus is Present",
 "TaxonKey is Atrytonopsis hianna (Scudder, 1863)",
 "Year 2004-2024"
 ]
}

 The dataset includes 1127 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024545-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/t9slu5
title: 614_1_NL_Fl_H_109_067.tab
DOI: 10.57745/oxpv0m
title: 614_2_NL_Fl_H_109_067.tab
DOI: 10.57745/qydlyn
title: 614_3_NL_Fl_H_109_067.tab
DOI: 10.13023/2024.alum.23
title: Mango and Mango Juice

description: Includes data on the aluminum content of mango and mango juice.
DOI: 10.57745/rygjam
title: 616_2_Cal_STD_109_067.tab
DOI: 10.57745/ebxmqr
title: 617_1_Ref_V_110_068.tab
DOI: 10.57745/comcrw
title: 617_2_Ref_V_110_068.tab
DOI: 10.57745/vacuyy
title: 617_3_Ref_V_110_068.tab
DOI: 10.57745/jlmaka
title: 618_1_Ref_T_110_068.tab
DOI: 10.57745/6iwixx
title: 618_2_Ref_T_110_068.tab
DOI: 10.57745/mvgppj
title: 618_3_Ref_T_110_068.tab
DOI: 10.15468/dl.k3wdcb
title: Occurrence Download

description: A dataset containing 1259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Observation, Specimen, Occurrence evidence)",
 "Continent is North America",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhionaeschna mutata (Hagen, 1861)",
 "Year 2004-2024"
 ]
}

 The dataset includes 1259 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024546-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/bzrqu9
title: 619_1_L_Fr_iBCC_110_068.tab
DOI: 10.57745/xlayjg
title: 619_2_L_Fr_iBCC_110_068.tab
DOI: 10.57745/cownnj
title: 619_3_L_Fr_iBCC_110_068.tab
DOI: 10.25573/serc.27871395
title: 2018-2024 L0 Water Quality Data from Marsh Outlet Exo Sonde, Smithsonian Environmental Research Center

description: This is 2018-2024 L0 water quality data from a YSI Exo Sonde located in the marsh outlet at the Global Change Research Wetland (GCREW) at the Smithsonian Environmental Research Center. The data are raw and have not undergone QA/QC processing and should therefore be used with caution.
DOI: 10.25573/serc.27871395.v1
title: 2018-2024 L0 Water Quality Data from Marsh Outlet Exo Sonde, Smithsonian Environmental Research Center

description: This is 2018-2024 L0 water quality data from a YSI Exo Sonde located in the marsh outlet at the Global Change Research Wetland (GCREW) at the Smithsonian Environmental Research Center. The data are raw and have not undergone QA/QC processing and should therefore be used with caution.
DOI: 10.57745/w6leb6
title: 6_1_NL_Jl_H_20_011.tab
DOI: 10.57745/vsj8i3
title: 62_033.json
DOI: 10.57745/cdz3cq
title: 62_1_JL_Bl_isSCC_29_016.tab
DOI: 10.57745/6owt6h
title: 622_1_NL_Fr_H_110_068.tab
DOI: 10.57745/hytihw
title: 622_2_NL_Fr_H_110_068.tab
DOI: 10.57745/x2wqmv
title: 622_3_NL_Fr_H_110_068.tab
DOI: 10.57745/qvxpvg
title: 62_2_JL_Bl_isSCC_29_016.tab
DOI: 10.57745/knwxlf
title: 623_1_NL_Fr_H_110_068.tab
DOI: 10.57745/xespy4
title: 623_2_NL_Fr_H_110_068.tab
DOI: 10.57745/kgy0qq
title: 623_3_NL_Fr_H_110_068.tab
DOI: 10.57745/0d5xgc
title: 62_3_JL_Bl_isSCC_29_016.tab
DOI: 10.57745/h2b8lu
title: 624_1_L_Fl_EL_110_068.tab
DOI: 10.57745/573kxu
title: 624_2_L_Fl_EL_110_068.tab
DOI: 10.57745/jis6zs
title: 624_3_L_Fl_EL_110_068.tab
DOI: 10.13023/2024.alum.24
title: Melons

description: Includes data on the aluminum content of melons.
DOI: 10.57745/vum4ky
title: 626_2_Cal_STD_110_068.tab
DOI: 10.57745/znlexu
title: 627_1_Ref_V_111_069.tab
DOI: 10.15468/dl.n6za4k
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-94.0",
 "Geometry POLYGON((0.08662 42.36936,2.02907 42.36936,2.02907 43.56855,0.08662 43.56855,0.08662 42.36936))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Impatiens glandulifera Royle, Buddleja davidii Franch., Orconectes limosus (Rafinesque, 1817), Procambarus clarkii (Girard, 1852), Ailanthus altissima (Mill.) Swingle, Senecio inaequidens DC., Ailanthus altissima (Mill.) Swingle)",
 "Year 1000-2000"
 ]
}

 The dataset includes 55 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024547-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/8en9uv
title: 627_2_Ref_V_111_069.tab
DOI: 10.57745/ak6s2n
title: 627_3_Ref_V_111_069.tab
DOI: 10.57745/uxx59t
title: 628_1_Ref_T_111_069.tab
DOI: 10.57745/zf5szy
title: 628_2_Ref_T_111_069.tab
DOI: 10.57745/7acxaj
title: 628_3_Ref_T_111_069.tab
DOI: 10.57745/xks3ow
title: 629_1_L_Fr_nBCC_111_069.tab
DOI: 10.57745/gwocry
title: 629_2_L_Fr_nBCC_111_069.tab
DOI: 10.57745/lr1ej9
title: 629_3_L_Fr_nBCC_111_069.tab
DOI: 10.15468/dl.7t29v6
title: Occurrence Download

description: A dataset containing 240 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((33.58522 45.80027,41.76189 45.80027,41.76189 53.58625,33.58522 53.58625,33.58522 45.80027))",
 "OccurrenceStatus is Present",
 "TaxonKey is Serinus serinus (Linnaeus, 1766)"
 ]
}

 The dataset includes 240 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024548-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/oal9qv
title: 6_2_NL_Jl_H_20_011.tab
DOI: 10.57745/oavmpu
title: 63_034.json
DOI: 10.57745/spsgxt
title: 632_1_NL_Fr_H_111_069.tab