cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/cbt-kxy-zrp
title: Erodium cicutarium (L.) L'Hér.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/hh4-2yw-60j
title: Mentha longifolia (L.) L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/bza-7ws-hwj
title: Hypericum scabrum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/pgr-fgp-1mn
title: Trifolium arvense L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/ly3-1tj-m3r
title: Thymus L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/g3n-v6l-qb5
title: Euphorbia szovitsii Fisch. & C.A.Mey.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/n22-fx3-e01
title: Onosma microcarpa Steven ex A. DC.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/2zr-l8r-y1v
title: Euphorbia marschalliana subsp. marschalliana

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7ck-wlv-df4
title: Malvaceae Juss.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/jb3-d2r-63m
title: Petrorhagia cretica (L.) P. W. Ball & Heywood

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/vcj-26v-jrk
title: Tamarix L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7wt-tbl-831
title: Thalictrum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/a7e-9ht-epf
title: Prunus nairica (Fed. & Takht.) Eisenman

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/xlw-ary-tx2
title: Dysphania botrys (L.) Mosyakin & Clemants

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/nmj-qp5-px1
title: Galium humifusum M.Bieb.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/tr6-wpj-nzh
title: Rubia tinctorum L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/7d5-lmv-fy5
title: Sedum album L.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.3535/wcg-q47-12t
title: Oeosporangium persicum (Bory) Vis.

description: Digital Specimen for the physical specimen hosted at University of Graz.
DOI: 10.15468/dl.2kxrg2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Buteo platypterus)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002794-250717081556266/datasets/export for details.
DOI: 10.15468/dl.x2rsxj
title: Occurrence Download

description: A dataset containing 531 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 531 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002796-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qffba7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Artemisia verlotiorum",
 "Country is Denmark",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002797-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nq3v3f
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Artemisia verlotiorum",
 "Country is Georgia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002799-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzx5gh
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Helianthus tuberosus",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 22 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002798-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a23jtv
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Draba koeiei"
 ]
}

 The dataset includes 37 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002800-250717081556266/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.29218013
title: Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference

description: Regionalization of intensive care for premature infants refers to a triage system that directs mothers to hospitals with varying capabilities based on the risks their babies face. Given the limited capacity of highly specialized hospitals, understanding the impact of delivering premature infants at these facilities on infant mortality could facilitate the design of a more efficient perinatal regionalization system. To address this, Baiocchi et al. proposed strengthening a continuous instrumental variable (IV) in an IV-based matched-pair design by focusing on a smaller cohort that could be paired with a larger separation in the IV dose. Three elements changed with the strengthened IV: the study cohort, compliance rate and latent complier subgroup. Here, we introduce a non-bipartite, template matching algorithm that strengthens the IV while maintaining fidelity to the original study cohort. We then study randomization-based and IV dose-dependent, biased randomization-based inference for partial identification bounds of the sample average treatment effect. We found that delivering preterm babies at a high-level, as opposed to a low-level, hospital reduced infant mortality rate for 163,532 mothers, whereas the treatment effect was minimal among a subgroup of non-black, low-risk mothers. 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.29218013.v1
title: Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference

description: Regionalization of intensive care for premature infants refers to a triage system that directs mothers to hospitals with varying capabilities based on the risks their babies face. Given the limited capacity of highly specialized hospitals, understanding the impact of delivering premature infants at these facilities on infant mortality could facilitate the design of a more efficient perinatal regionalization system. To address this, Baiocchi et al. proposed strengthening a continuous instrumental variable (IV) in an IV-based matched-pair design by focusing on a smaller cohort that could be paired with a larger separation in the IV dose. Three elements changed with the strengthened IV: the study cohort, compliance rate and latent complier subgroup. Here, we introduce a non-bipartite, template matching algorithm that strengthens the IV while maintaining fidelity to the original study cohort. We then study randomization-based and IV dose-dependent, biased randomization-based inference for partial identification bounds of the sample average treatment effect. We found that delivering preterm babies at a high-level, as opposed to a low-level, hospital reduced infant mortality rate for 163,532 mothers, whereas the treatment effect was minimal among a subgroup of non-black, low-risk mothers. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.f9ftef
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidium linearilobum"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002801-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57761/wxd6-jv90
title: Gallup World Poll

description: If you have questions or concerns, please contact Ron Nakao at ronbo@stanford.edu. 
description: Gallup’s World Poll continually surveys residents in more than 150 countries and areas, representing more than 98% of the world’s adult population, using randomly selected, nationally representative samples. Gallup typically surveys 1,000 individuals in each country or area, using a standard set of core questions that has been translated into the major languages of the respective country. In some regions, supplemental questions are asked in addition to core questions.
Face-to-face interviews are approximately 1 hour, while telephone interviews are about 30 minutes. In many countries, the survey is conducted once per year, and fieldwork is generally completed in two to four weeks. The Country Dataset Details document displays each country’s sample size, month/year of the data collection, mode of interviewing, languages employed, design effect, margin of error and details about sample coverage.

The data was last updated February 2025.
description: See ***Supporting files ***section (below).


description: See ***Supporting files ***section (below).
DOI: 10.6084/m9.figshare.29127860
title: Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression

description: This article develops methodology for higher order accurate two-sided Bootstrap confidence intervals (CIs) in high dimensional penalized regression models using the Bootstrap. We consider a large class of penalized regression methods that satisfy the Oracle property of <i>Fan and Li</i> and a stronger variant of it, called the Strong Oracle property. While second order accuracy of the Bootstrap is known for both classes, it is typically not sufficient to guarantee better accuracy of two-sided Bootstrap CIs over their Oracle limit based counterparts. In this article, we show that for penalization methods with the strong Oracle property (called “Class I” methods here), a variant of the two-sided symmetric Bootstrap CI method, originally proposed by <i>Hall</i> in the traditional <i>fixed</i> dimensional case, can attain an accuracy level of O(n−2) in sample size <i>n</i>, even if the dimension of the regression model grows at <i>an arbitrary</i> polynomial rate in <i>n</i>. On the other end, for penalized methods with only the Oracle property (called “Class II” methods here), two-sided symmetric Bootstrap CIs can achieve the same O(n−2) level of accuracy in such high dimensions only after some nontrivial modification. Consequently, the proposed methodology can be used to construct accurate two-sided CIs for the relevant regression parameters in very high dimensional regression models with a much smaller sample size than what has been considered possible in the literature. We also report results from a simulation study and illustrate the methodology with a real data example. 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.29127860.v1
title: Higher Order Accurate Symmetric Bootstrap Confidence Intervals in High Dimensional Penalized Regression

description: This article develops methodology for higher order accurate two-sided Bootstrap confidence intervals (CIs) in high dimensional penalized regression models using the Bootstrap. We consider a large class of penalized regression methods that satisfy the Oracle property of <i>Fan and Li</i> and a stronger variant of it, called the Strong Oracle property. While second order accuracy of the Bootstrap is known for both classes, it is typically not sufficient to guarantee better accuracy of two-sided Bootstrap CIs over their Oracle limit based counterparts. In this article, we show that for penalization methods with the strong Oracle property (called “Class I” methods here), a variant of the two-sided symmetric Bootstrap CI method, originally proposed by <i>Hall</i> in the traditional <i>fixed</i> dimensional case, can attain an accuracy level of O(n−2) in sample size <i>n</i>, even if the dimension of the regression model grows at <i>an arbitrary</i> polynomial rate in <i>n</i>. On the other end, for penalized methods with only the Oracle property (called “Class II” methods here), two-sided symmetric Bootstrap CIs can achieve the same O(n−2) level of accuracy in such high dimensions only after some nontrivial modification. Consequently, the proposed methodology can be used to construct accurate two-sided CIs for the relevant regression parameters in very high dimensional regression models with a much smaller sample size than what has been considered possible in the literature. We also report results from a simulation study and illustrate the methodology with a real data example. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.57761/58fh-rr29
title: Gallup Panel COVID-19 and Wellbeing Survey

description: If you have questions or concerns, please contact Ron Nakao at ronbo@stanford.edu.


description: The COVID-19 web survey has been utilized to track American attitudes on topics related to the COVID-19 pandemic, including well-being. The survey began fielding on March 13, 2020, with daily random samples of U.S. adults, aged 18 and older, who are members of the Gallup Panel. Approximately 1,200 daily completes were collected from March 13 through April 26, 2020. From April 27 to August 16, 2020, approximately 500 daily completes were collected. Starting August 17, 2020, the survey moved from daily surveying to a survey conducted one time per month over a two-week field period (typically the last two weeks of the month). Beginning in 2022, the COVID survey moved to quarterly data collection.

The Gallup Panel COVID-19 Survey table includes survey responses from March 2020 through Q1 2023. Starting in Q2 2023, the original COVID-19 survey was narrowed down to serve as a wellbeing-focused survey (see Gallup Panel Wellbeing Survey table).
description: Results for this Gallup poll are based on self-administered web surveys conducted with a random sample of U.S. adults aged 18 and older, who are members of the Gallup Panel. The survey was conducted in English. Individuals without Internet access were not covered by this study.

The Gallup Panel is a probability-based, nationally representative panel of U.S. adults. Members are randomly selected using random-digit-dial phone interviews that cover landline and cellphones and address-based sampling methods. The Gallup Panel is not an opt-in panel.



Gallup weights the obtained samples each day to adjust for the probability of select and to correct for nonresponse bias. Nonresponse adjustments are made by adjusting the sample to match the national demographics of gender, age, race, Hispanic ethnicity, education and region. Demographic weighting targets are based on the most recent Current Population Survey figures for the aged-18-and-older U.S. population. Respondents receive a small post-paid incentive of $1 incentive for completing the survey.


description: For more information about methodology and included variables, please see ***Supporting Files****.*
DOI: 10.6084/m9.figshare.29281650
title: A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling

description: Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its nonrandom sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the accuracy of the reported degree is questionable due to reporting errors, evidenced through an unusual frequency of multiples of five and improbably large values. To address this measurement error, we leverage a byproduct of the RDS process (e.g., respondents’ recruitment patterns) and develop a novel degree estimator based on a latent variable model of the true degree that accounts for response errors via a reporting mechanism and incorporates recruitment information and external demographic profiles. The effectiveness of the proposed method is demonstrated through a case study and a simulation study, which shows accurate and reliable degree estimates leading to significant improvements in population parameter estimation. 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.29281650.v1
title: A Latent Variable Model for Individual Degree Measures in Respondent-Driven Sampling

description: Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its nonrandom sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the accuracy of the reported degree is questionable due to reporting errors, evidenced through an unusual frequency of multiples of five and improbably large values. To address this measurement error, we leverage a byproduct of the RDS process (e.g., respondents’ recruitment patterns) and develop a novel degree estimator based on a latent variable model of the true degree that accounts for response errors via a reporting mechanism and incorporates recruitment information and external demographic profiles. The effectiveness of the proposed method is demonstrated through a case study and a simulation study, which shows accurate and reliable degree estimates leading to significant improvements in population parameter estimation. 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.29218004.v1
title: Deep Mutual Density Ratio Estimation with Bregman Divergence and Its Applications

description: This article introduces a unified approach to estimating the mutual density ratio, defined as the ratio between the joint density function and the product of the individual marginal density functions of two random vectors. It serves as a fundamental measure for quantifying the relationship between two random vectors. Our method uses the Bregman divergence to construct the objective function and leverages deep neural networks to approximate the logarithm of the mutual density ratio. We establish a non-asymptotic error bound for our estimator, achieving the optimal minimax rate of convergence under a bounded support condition. Additionally, our estimator mitigates the curse of dimensionality when the distribution is supported on a lower-dimensional manifold. We extend our results to overparameterized neural networks and the case with unbounded support. Applications of our method include conditional probability density estimation, mutual information estimation, and independence testing. Simulation studies and real data examples demonstrate the effectiveness of our approach. 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.29218004
title: Deep Mutual Density Ratio Estimation with Bregman Divergence and Its Applications

description: This article introduces a unified approach to estimating the mutual density ratio, defined as the ratio between the joint density function and the product of the individual marginal density functions of two random vectors. It serves as a fundamental measure for quantifying the relationship between two random vectors. Our method uses the Bregman divergence to construct the objective function and leverages deep neural networks to approximate the logarithm of the mutual density ratio. We establish a non-asymptotic error bound for our estimator, achieving the optimal minimax rate of convergence under a bounded support condition. Additionally, our estimator mitigates the curse of dimensionality when the distribution is supported on a lower-dimensional manifold. We extend our results to overparameterized neural networks and the case with unbounded support. Applications of our method include conditional probability density estimation, mutual information estimation, and independence testing. Simulation studies and real data examples demonstrate the effectiveness of our approach. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.