cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ga65nx
title: Occurrence Download

description: A dataset containing 160 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elassodiscus caudatus (Gilbert, 1915)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 160 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020377-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sbu6sw
title: Occurrence Download

description: A dataset containing 9585 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "TaxonKey is Apis cerana Fabricius, 1793"
 ]
}

 The dataset includes 9585 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020374-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.96duyy
title: Occurrence Download

description: A dataset containing 12822 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Alces alces (Linnaeus, 1758)"
 ]
 },
 "HasCoordinate is TRUE",
 "Year is greater than or equal to 2005",
 "Year is less than or equal to 2023",
 "DecimalLatitude is greater than or equal to 37.41520435458503",
 "DecimalLatitude is less than or equal to 64.72689397480589",
 "DecimalLongitude is greater than or equal to -119.44008206509096",
 "DecimalLongitude is less than or equal to -10.419110786346884",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 12822 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020376-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3u5t53
title: Occurrence Download

description: A dataset containing 1332 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Myrmecocystus mexicanus Wesmael, 1838"
 ]
}

 The dataset includes 1332 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020378-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k8sxmd
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus serrulata Lindl.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 7 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020379-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gh7vj5
title: Occurrence Download

description: A dataset containing 1629908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "Year 2020-2024"
 ]
}

 The dataset includes 1629908 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020322-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g5kzgn
title: Occurrence Download

description: A dataset containing 117 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (Russian Federation, Ukraine)",
 "DatasetKey is Moscow University Herbarium (MW)",
 "Geometry POLYGON((38.93552 57.8744,32.2102 56.23044,27.92478 50.04895,32.39111 47.67264,41.28938 51.18645,40.87839 55.81944,38.93552 57.8744))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Barbarea vulgaris (L.) W.T.Aiton"
 ]
}

 The dataset includes 117 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020380-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wvrwsg
title: Occurrence Download

description: A dataset containing 484 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus mohriana Buckley ex Rydb."
 ]
}

 The dataset includes 484 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020381-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.faqbt8
title: Occurrence Download

description: A dataset containing 769605 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((137.7491 43.59929,143.7229 55.54688,132.64403 71.05944,101.90633 74.96171,68.30374 71.477,39.18149 67.74338,27.2339 58.53378,26.17527 48.16631,25.39126 40.01259,27.73171 33.64296,35.94568 26.17572,42.6662 13.48141,49.38672 11.73905,77.01552 6.51198,83.48713 11.98796,91.20328 16.2194,103.15087 9.74779,108.62685 7.2587,115.84519 20.45083,122.81461 31.15388,137.7491 43.59929))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Fabaceae",
 "Year is greater than or equal to 1970"
 ]
}

 The dataset includes 769605 records from 520 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020321-241126133413365/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.26508976
title: Multi-Offset Synthetic GPR Data

description: <b>Overview</b>This repository contains the data and scripts related to the paper titled "A realistic 2D multi-offset, multi-frequency synthetic GPR data set as a benchmark for testing new algorithms" The data set includes synthetic GPR data generated using gprMax, detailed descriptions of the acquisition geometry, and the required Conda environment for running the associated Jupyter notebooks.<br><b>Repository Contents</b><br>- <code>env.yml</code> Conda environment file listing all dependencies required to run the Jupyter notebooks.- <code>notebooks</code> Directory containing Jupyter notebooks to open and validate the data files.- <code>data</code> Directory containing the synthetic GPR data files.<br><br><b>GPR Data</b>The synthetic GPR data set was generated using the gprMax software, which simulates electromagnetic wave propagation using the Finite-Difference Time-Domain (FDTD) method. The data set includes multiple 2D profiles with varying offsets and frequencies, designed to test and evaluate processing, analysis, and inversion routines for GPR data.<b>Acquisition Geometry</b>For each of the four sections selected within the model, 161 shots and 161 receivers were used, covering different frequencies (50 MHz, 100 MHz, and 200 MHz). Detailed sketches and validation methods are provided within the Jupyter notebooks.<br><b>Usage Notes</b>An example Python code to read the header file and additional usage notes can be found within the Jupyter notebooks provided in this repository.
DOI: 10.15468/dl.2mb3mh
title: Occurrence Download

description: A dataset containing 1325 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lampanyctus ritteri Gilbert, 1915",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1325 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020382-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3rwjqu
title: Occurrence Download

description: A dataset containing 3157 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Quercus stellata Wangenh."
 ]
}

 The dataset includes 3157 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020384-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6nvn82
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus triloba Lindl.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020385-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2vc7kf
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Prunus virginiana L.",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is Living Specimen"
 ]
}

 The dataset includes 77 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020387-241126133413365/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.27605050
title: Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies

description: Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale testing of composite null hypotheses. The goal is to determine whether all null hypotheses—as opposed to at least one—in a set of individual tests should simultaneously be rejected. Recently, various methods have been proposed for each of these situations, including an appealing two-group empirical Bayes approach that calculates local false discovery rates (lfdr). However, lfdr estimation is difficult due to the need for multivariate density estimation. Furthermore, the multiple testing rules for the empirical Bayes lfdr approach can disagree with conventional frequentist z-statistics, which is troubling for a field that ubiquitously uses summary statistics. This work proposes a framework to unify two-group testing in genetic association composite null settings, the conditionally symmetric multidimensional Gaussian mixture model (csmGmm). The csmGmm is shown to demonstrate more robust operating characteristics than recently-proposed alternatives. Crucially, the csmGmm also offers interpretability guarantees by harmonizing lfdr and z-statistic testing rules. We extend the base csmGmm to cover each of the mediation, pleiotropy, and replication settings, and we prove that the lfdr z-statistic agreement holds in each situation. We apply the model to a collection of translational lung cancer genetic association studies that motivated this work. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27605050.v1
title: Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies

description: Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale testing of composite null hypotheses. The goal is to determine whether all null hypotheses—as opposed to at least one—in a set of individual tests should simultaneously be rejected. Recently, various methods have been proposed for each of these situations, including an appealing two-group empirical Bayes approach that calculates local false discovery rates (lfdr). However, lfdr estimation is difficult due to the need for multivariate density estimation. Furthermore, the multiple testing rules for the empirical Bayes lfdr approach can disagree with conventional frequentist z-statistics, which is troubling for a field that ubiquitously uses summary statistics. This work proposes a framework to unify two-group testing in genetic association composite null settings, the conditionally symmetric multidimensional Gaussian mixture model (csmGmm). The csmGmm is shown to demonstrate more robust operating characteristics than recently-proposed alternatives. Crucially, the csmGmm also offers interpretability guarantees by harmonizing lfdr and z-statistic testing rules. We extend the base csmGmm to cover each of the mediation, pleiotropy, and replication settings, and we prove that the lfdr z-statistic agreement holds in each situation. We apply the model to a collection of translational lung cancer genetic association studies that motivated this work. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.mrnssg
title: Occurrence Download

description: A dataset listing the 255 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "GadmGid is BRA.8_1",
 "TaxonKey is Mammalia"
 ]
}

 The dataset's 255 records were derived from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020335-241126133413365/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.27054805
title: Zigzag Path Connects Two Monte Carlo Samplers: Hamiltonian Counterpart to a Piecewise Deterministic Markov Process

description: Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The position and velocity component of the corresponding Hamiltonian dynamics travels along a zigzag path paralleling the Markovian zigzag process; however, the dynamics is non-Markovian in this position-velocity space as the momentum component encodes non-immediate pasts. This information is partially lost during a momentum refreshment step, in which we preserve its direction but resample magnitude. In the limit of increasingly frequent momentum refreshments, we prove that Hamiltonian zigzag converges strongly to its Markovian counterpart. This theoretical insight suggests that, when retaining full momentum information, Hamiltonian zigzag can better explore target distributions with highly correlated parameters by suppressing the diffusive behavior of Markovian zigzag. We corroborate this intuition by comparing performance of the two zigzag cousins on high-dimensional truncated multivariate Gaussians, including a 11,235-dimensional target arising from a Bayesian phylogenetic multivariate probit modeling of HIV virus data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.4whssg
title: Occurrence Download

description: A dataset containing 2782 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lycodapus fierasfer Gilbert, 1890",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2782 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020389-241126133413365/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.27054805.v2
title: Zigzag Path Connects Two Monte Carlo Samplers: Hamiltonian Counterpart to a Piecewise Deterministic Markov Process

description: Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The position and velocity component of the corresponding Hamiltonian dynamics travels along a zigzag path paralleling the Markovian zigzag process; however, the dynamics is non-Markovian in this position-velocity space as the momentum component encodes non-immediate pasts. This information is partially lost during a momentum refreshment step, in which we preserve its direction but resample magnitude. In the limit of increasingly frequent momentum refreshments, we prove that Hamiltonian zigzag converges strongly to its Markovian counterpart. This theoretical insight suggests that, when retaining full momentum information, Hamiltonian zigzag can better explore target distributions with highly correlated parameters by suppressing the diffusive behavior of Markovian zigzag. We corroborate this intuition by comparing performance of the two zigzag cousins on high-dimensional truncated multivariate Gaussians, including a 11,235-dimensional target arising from a Bayesian phylogenetic multivariate probit modeling of HIV virus data. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27054805.v1
title: Zigzag path connects two Monte Carlo samplers: Hamiltonian counterpart to a piecewise deterministic Markov process

description: Zigzag and other piecewise deterministic Markov process samplers have attracted significant interest for their non-reversibility and other appealing properties for Bayesian posterior computation. Hamiltonian Monte Carlo is another state-of-the-art sampler, exploiting fictitious momentum to guide Markov chains through complex target distributions. We establish an important connection between the zigzag sampler and a variant of Hamiltonian Monte Carlo based on Laplace-distributed momentum. The position and velocity component of the corresponding Hamiltonian dynamics travels along a zigzag path paralleling the Markovian zigzag process; however, the dynamics is non-Markovian in this position-velocity space as the momentum component encodes non-immediate pasts. This information is partially lost during a momentum refreshment step, in which we preserve its direction but re-sample magnitude. In the limit of increasingly frequent momentum refreshments, we prove that Hamiltonian zigzag converges strongly to its Markovian counterpart. This theoretical insight suggests that, when retaining full momentum information, Hamiltonian zigzag can better explore target distributions with highly correlated parameters by suppressing the diffusive behavior of Markovian zigzag. We corroborate this intuition by comparing performance of the two zigzag cousins on high-dimensional truncated multivariate Gaussians, including a 11,235-dimensional target arising from a Bayesian phylogenetic multivariate probit modeling of hiv virus data.
DOI: 10.6084/m9.figshare.27968938
title: On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination

description: There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario involving a source dataset and a target dataset. We first design six settings under covariate shift and conduct a comparative analysis by deriving the semiparametric efficiency bounds for the ATE in the target population. We then extend this analysis to six new settings that incorporate both covariate shift and posterior drift. Our study uncovers the key factors that influence efficiency gains and the “effective sample size” when combining two datasets, with a particular emphasis on the roles of the variance ratio of potential outcomes between two datasets and the derivatives of the posterior drift function. To the best of our knowledge, this is the first paper that explicitly explores the role of the posterior drift functions in causal inference. Additionally, we also propose novel methods for conducting sensitivity analysis to address violations of transportability between two datasets. We empirically validate our findings by constructing locally efficient estimators and conducting extensive simulations. We demonstrate the proposed methods in two real-world applications.
DOI: 10.6084/m9.figshare.27968938.v1
title: On the Comparative Analysis of Average Treatment Effects Estimation via Data Combination

description: There is growing interest in exploring causal effects in target populations via data combination. However, most approaches are tailored to specific settings and lack comprehensive comparative analyses. In this article, we focus on a typical scenario involving a source dataset and a target dataset. We first design six settings under covariate shift and conduct a comparative analysis by deriving the semiparametric efficiency bounds for the ATE in the target population. We then extend this analysis to six new settings that incorporate both covariate shift and posterior drift. Our study uncovers the key factors that influence efficiency gains and the “effective sample size” when combining two datasets, with a particular emphasis on the roles of the variance ratio of potential outcomes between two datasets and the derivatives of the posterior drift function. To the best of our knowledge, this is the first paper that explicitly explores the role of the posterior drift functions in causal inference. Additionally, we also propose novel methods for conducting sensitivity analysis to address violations of transportability between two datasets. We empirically validate our findings by constructing locally efficient estimators and conducting extensive simulations. We demonstrate the proposed methods in two real-world applications.
DOI: 10.15468/dl.v8ggwa
title: Occurrence Download

description: A dataset containing 356 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "OccurrenceStatus is Present",
 "TaxonKey is Neosuthora davidiana (Slater, 1897)",
 "Year 2000-2024"
 ]
}

 The dataset includes 356 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020390-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.