cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.be84r2
title: Occurrence Download

description: A dataset containing 2592699 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Pl@ntNet observations"
 ]
}

 The dataset includes 2592699 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011798-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.1594/pangaea.989435
title: Hydrogen isotopes of n-alkane biomarkers and brGDGT temperatures from Lake Tulane, Florida, USA

description: This dataset includes temperatures derived from branched glycerol dialkyl glycerol tetraethers (brGDGTs) and regional hydrology changes reconstructed from hydrogen isotope ratios (δ2H) of n-alkanes extracted from a lacustrine sediment core from Lake Tulane, Florida, USA. In 1994, 8 cores from 6 locations (2 complete overlaps) were acquired from a transect along the north basin of Lake Tulane. Cores were taken with a 7.5-cm diameter Wright square-rod piston corer from an anchored raft. Separately, a single surface-water interface core was acquired from the north basin of Lake Tulane in 2010. brGDGTs and n-alkanes were extracted from archived core sections at 8-10 cm intervals in August of 2018 and from the surface-water interface core at 5 cm intervals in August of 2019. The archived core interval spanned Heinrich Stadials 4-2 (41,000-17,000 BP) and the surface-water interface core spanned 78-0 BP. brGDGT distributions from both cores were measured via atmospheric pressure chemical ionization high-performance liquid chromatography-mass spectrometry (APCI/HPLC–MS), and hydrogen isotope ratios of n-alkanes were measured on a Thermo Trace GC Ultra coupled to a Thermo Electron Delta V Advantage IRMS with an Isolink combustion furnace. The brGDGT data was converted to degrees Celsius (°C) following the open-sourced code provided in O'Beirne et al. (2023, doi:10.1016/j.gca.2023.08.019). Hydrogen isotope results are given in the conventional δ-notation. A proxy for seasonal precipitation and aridity (ε(C29-C23)) was created, which reflects the difference between the δ2H values of aquatic n-alkanes (δ2HC23) and terrestrial leaf wax δ2H values (δ2HC29) using the following equations: (1) α = (1000 + 𝛿2HC29)/(1000 + 𝛿2HC23), and (2) ε(C29-C23) = (α -1) x 1000.
DOI: 10.6084/m9.figshare.25222262
title: MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information

description: In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization problem by using auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality. Unlike existing methods that mainly rely on simple linear models to combine auxiliary information with the main data matrix, we propose to integrate gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information (MFAI). Thus, MFAI naturally inherits several salient features of gradient boosted trees, such as the capability of flexibly modeling nonlinear relationships and robustness to irrelevant features and missing values in auxiliary information. The parameters in MFAI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting. Moreover, MFAI is computationally efficient and scalable to large datasets by exploiting variational inference. We demonstrate the advantages of MFAI through comprehensive numerical results from simulation studies and real data analyses. Our approach is implemented in the R package <i>mfair</i> available at https://github.com/YangLabHKUST/mfair. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25222262.v1
title: MFAI: A Scalable Bayesian Matrix Factorization Approach to Leveraging Auxiliary Information

description: In various practical situations, matrix factorization methods suffer from poor data quality, such as high data sparsity and low signal-to-noise ratio (SNR). Here, we consider a matrix factorization problem by using auxiliary information, which is massively available in real-world applications, to overcome the challenges caused by poor data quality. Unlike existing methods that mainly rely on simple linear models to combine auxiliary information with the main data matrix, we propose to integrate gradient boosted trees in the probabilistic matrix factorization framework to effectively leverage auxiliary information (MFAI). Thus, MFAI naturally inherits several salient features of gradient boosted trees, such as the capability of flexibly modeling nonlinear relationships and robustness to irrelevant features and missing values in auxiliary information. The parameters in MFAI can be automatically determined under the empirical Bayes framework, making it adaptive to the utilization of auxiliary information and immune to overfitting. Moreover, MFAI is computationally efficient and scalable to large datasets by exploiting variational inference. We demonstrate the advantages of MFAI through comprehensive numerical results from simulation studies and real data analyses. Our approach is implemented in the R package <i>mfair</i> available at https://github.com/YangLabHKUST/mfair. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25664524
title: A Plot is Worth a Thousand Tests: Assessing Residual Diagnostics with the Lineup Protocol

description: Regression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems with a model fit. Here we provide evidence for why this is good advice using data from a visual inference experiment. We show how conventional tests are too sensitive, which means that too often the conclusion would be that the model fit is inadequate. The experiment uses the lineup protocol which puts a residual plot in the context of null plots. This helps generate reliable and consistent reading of residual plots for better model diagnosis. It can also help in an obverse situation where a conventional test would fail to detect a problem with a model due to contaminated data. The lineup protocol also detects a range of departures from good residuals simultaneously. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.25664524.v1
title: A Plot is Worth a Thousand Tests: Assessing Residual Diagnostics with the Lineup Protocol

description: Regression experts consistently recommend plotting residuals for model diagnosis, despite the availability of many numerical hypothesis test procedures designed to use residuals to assess problems with a model fit. Here we provide evidence for why this is good advice using data from a visual inference experiment. We show how conventional tests are too sensitive, which means that too often the conclusion would be that the model fit is inadequate. The experiment uses the lineup protocol which puts a residual plot in the context of null plots. This helps generate reliable and consistent reading of residual plots for better model diagnosis. It can also help in an obverse situation where a conventional test would fail to detect a problem with a model due to contaminated data. The lineup protocol also detects a range of departures from good residuals simultaneously. Supplemental materials for the article are available online.
DOI: 10.6084/m9.figshare.25222258
title: Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas

description: Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This article considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. This implicit copula extension is an important contribution of our work, and improves the accuracy of SDGM approximations for only a modest increase in computational cost. Our parameterization of the copula approximation is novel, even in the Gaussian case. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models. Supplemental materials including code and appendix are available online.
DOI: 10.6084/m9.figshare.25222258.v1
title: Structured variational approximations with skew normal decomposable graphical models and implicit copulas

description: Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. This implicit copula extension is an important contribution of our work, and improves the accuracy of SDGM approximations for only a modest increase in computational cost. Our parametrization of the copula approximation is novel, even in the Gaussian case. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models. Supplemental materials including code and appendix are available online.
DOI: 10.6084/m9.figshare.25222258.v2
title: Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas

description: Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This article considers approximate inference methods for complex latent variable models where the posterior is close to Gaussian, but with some skewness in the posterior marginals. We consider skew decomposable graphical models (SDGMs), which are based on the closed skew normal family of distributions, as variational approximations. These approximations can reflect the true posterior conditional independence structure and capture posterior skewness. To increase flexibility, implicit copula SDGM approximations are also developed, where elementwise transformations of an approximately standardized SDGM random vector are considered. This implicit copula extension is an important contribution of our work, and improves the accuracy of SDGM approximations for only a modest increase in computational cost. Our parameterization of the copula approximation is novel, even in the Gaussian case. Performance of the methods is examined in a number of real examples involving generalized linear mixed models and state space models. Supplemental materials including code and appendix are available online.
DOI: 10.6084/m9.figshare.25871326
title: Nonparametric High-Dimensional Multi-Sample Tests based on Graph Theory

description: High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introduce two key properties desirable for graphs in testing homogeneity. Roughly speaking, these properties may be described as: unboundedness of edge counts under the same distribution and boundedness of edge counts under different distributions. It turns out that the minimum spanning tree violates these properties but the shortest Hamiltonian path posses them. Based on the shortest Hamiltonian path, we propose two combinations of edge counts in multiple samples to test for homogeneity. We give the permutation null distributions of proposed statistics when sample sizes go to infinity. The power is analyzed by assuming both sample sizes and dimensionality tend to infinity. Simulations show that our new tests behave very well overall in comparison with various competitors. Real data analysis of tumors and images further convince the value of our proposed tests. Software implementing the test is available in the R package GRelevance. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.25871326.v2
title: Nonparametric High-Dimensional Multi-Sample Tests based on Graph Theory

description: High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introduce two key properties desirable for graphs in testing homogeneity. Roughly speaking, these properties may be described as: unboundedness of edge counts under the same distribution and boundedness of edge counts under different distributions. It turns out that the minimum spanning tree violates these properties but the shortest Hamiltonian path posses them. Based on the shortest Hamiltonian path, we propose two combinations of edge counts in multiple samples to test for homogeneity. We give the permutation null distributions of proposed statistics when sample sizes go to infinity. The power is analyzed by assuming both sample sizes and dimensionality tend to infinity. Simulations show that our new tests behave very well overall in comparison with various competitors. Real data analysis of tumors and images further convince the value of our proposed tests. Software implementing the test is available in the R package GRelevance. Supplemental materials for this article are available online.
DOI: 10.6084/m9.figshare.25871326.v1
title: Nonparametric high-dimensional multi-sample tests based on graph theory

description: High-dimensional data pose unique challenges for data processing in an era of ever-increasing amounts of data availability. Graph theory can provide a structure of high-dimensional data. We introduce two key properties desirable for graphs in testing homogeneity. Roughly speaking, these properties may be described as: unboundedness of edge counts under the same distribution and boundedness of edge counts under different distributions. It turns out that the minimum spanning tree violates these properties but the shortest Hamiltonian path posses them. Based on the shortest Hamiltonian path, we propose two combinations of edge counts in multiple samples to test for homogeneity. We give the permutation null distributions of proposed statistics when sample sizes go to infinity. The power is analyzed by assuming both sample sizes and dimensionality tend to infinity. Simulations show that our new tests behave very well overall in comparison with various competitors. Real data analysis of tumors and images further convince the value of our proposed tests. Software implementing the test is available in the R package GRelevance. Supplemental materials are available online.
DOI: 10.15468/dl.wvkgr4
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Rheum compactum"
 ]
}

 The dataset includes 9 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011788-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zz3wmh
title: Occurrence Download

description: A dataset containing 2311829 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Museum of Comparative Zoology, Harvard University"
 ]
}

 The dataset includes 2311829 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011799-260120142942310/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.28715504
title: Simultaneous Outlier Detection and Prediction for Kriging with True Identification

description: Kriging with interpolation is widely used in various noise-free areas, such as computer experiments. However, owing to its Gaussian assumption, it is susceptible to outliers, which affects statistical inference, and the resulting conclusions could be misleading. Little work has explored outlier detection for kriging. Therefore, we propose a novel kriging method for simultaneous outlier detection and prediction by introducing a normal-gamma prior, which results in an unbounded penalty on the biases to distinguish outliers from normal data points. We develop a simple and efficient method, avoiding the expensive computation of the Markov chain Monte Carlo algorithm, to simultaneously detect outliers and make a prediction. We establish the true identification property for outlier detection and the consistency of the estimated hyperparameters in kriging under the increasing domain framework as if the number and locations of the outliers were known in advance. Under appropriate regularity conditions, we demonstrate information consistency for prediction in the presence of outliers. Numerical studies and real data examples show that the proposed method generally provides robust analyses in the presence of outliers.
DOI: 10.6084/m9.figshare.28715504.v1
title: Simultaneous Outlier Detection and Prediction for Kriging with True Identification

description: Kriging with interpolation is widely used in various noise-free areas, such as computer experiments. However, owing to its Gaussian assumption, it is susceptible to outliers, which affects statistical inference, and the resulting conclusions could be misleading. Little work has explored outlier detection for kriging. Therefore, we propose a novel kriging method for simultaneous outlier detection and prediction by introducing a normal-gamma prior, which results in an unbounded penalty on the biases to distinguish outliers from normal data points. We develop a simple and efficient method, avoiding the expensive computation of the Markov chain Monte Carlo algorithm, to simultaneously detect outliers and make a prediction. We establish the true identification property for outlier detection and the consistency of the estimated hyperparameters in kriging under the increasing domain framework as if the number and locations of the outliers were known in advance. Under appropriate regularity conditions, we demonstrate information consistency for prediction in the presence of outliers. Numerical studies and real data examples show that the proposed method generally provides robust analyses in the presence of outliers.
DOI: 10.15468/86e968
title: Review of the Chinese species of the genus Scelimena Serville, 1838 (Tetrigidae Scelimeninae: Scelimenini)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Lao, Chuangyu, Kasalo, Niko, Gao, Fan, Deng, Weian, Skejo, Josip (2022): Review of the Chinese species of the genus Scelimena Serville, 1838 (Tetrigidae Scelimeninae: Scelimenini). Zootaxa 5200 (4): 321-343, DOI: 10.11646/zootaxa.5200.4.2AbstractA brief preliminary revision of the genus Scelimena Serville, 1838 (Tetrigidae: Scelimeninae: Scelimenini) from PR China is presented. Scelimena pyrroma Lao, Kasalo, Gao, Deng et Skejo sp. nov. is described from Hainan, Ding'an, based on a female holotype and a male paratype, and an additional photograph of a female in the natural habitat from Wuzhi National Nature Reserve (Wuzhi-shan Mountain). Specimens of this new species were hitherto reported from Hainan as S. dentiumeris (Hancock, 1907), a species endemic to Borneo. The new species is easily distinguished from its congeners by the presence of prominent yellow humeral tubercles and by the lateral pronotal spines directed forwards. The new species from Hainan and S. chinensis (Hancock, 1915) from Vietnam are assigned to Scelimena producta species group based on the similarity with S. producta (Serville, 1838), which is characterized by the humeral angles with tubercles, wide vertex, and toothed ventral margins of hind femora, while S. boettcheri Günther, 1938 from Palawan is assigned to Scelimena discalis species group, based on the tuberculated median carina. Scelimena dammermanni Günther, 1938, stat. nov., hitherto regarded as a subspecies of S. producta, is elevated to a species level because it lacks ventral spines on the hind legs, present in all S. producta individuals. New synonymy is established: Scelimena melli Günther, 1938, = Scelimena brevispina Cao et Zheng, 2011, syn. nov.; = Scelimena wulingshana Zheng, 1993, syn. nov.; = Eufalconoides guizhouensis Zheng et Shi, 2006, syn. nov. Scelimena melli, S. guangxiensis Zheng et Jiang, 1994 from southern China and S. kempi (Hancock, 1915) from NE India are assigned to S. bellula species group on the basis of similarity to S. bellula Storozhenko et Dawwrueng, 2015. Further research should determine whether S. spicupennis Zheng & Ou, 2003 represents a valid species or a synonym of another Scelimena species, as well as if S. nitidogranulosa Günther, 1938 and S. guangxiensis Zheng & Jiang, 1994 represent separate species or whether they are conspecific. Specimens of S. nitidogranulosa from Hainan reported by Liang & Zheng in 1998 are in fact Platygavialidium sinicum Günther, 1939. Scelimena songkrana Zha et Wen, 2017 is recorded from China for the first time. A key to Scelimena species found in PR China is also provided.
DOI: 10.15468/dl.vcshav
title: Occurrence Download

description: A dataset containing 15734 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pithecellobium dulce"
 ]
}

 The dataset includes 15734 records from 530 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011789-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18372263
title: Infrastructure and Human Development: Evidence from Japan's Subnational Human Development Index

description: This repository contains the data and replication codes used in the empirical analysis of the accompanying manuscript. The materials allow full replication of all tables reported in the paper.
DOI: 10.5281/zenodo.18372264
title: Infrastructure and Human Development: Evidence from Japan's Subnational Human Development Index

description: This repository contains the data and replication codes used in the empirical analysis of the accompanying manuscript. The materials allow full replication of all tables reported in the paper.