cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7910/dvn/delg1v
title: Inferring Morphology and Strength of Magnetic Fields From Proton Radiographs

description: Proton radiography is an important diagnostic method for laser plasma experiments, and is particularly
important in the analysis of magnetized plasmas. The theory of radiographic image analysis has heretofore
only permitted somewhat limited analysis of the radiographs of such plasmas. We furnish here a theory
that remedies this deficiency. We show that to linear order in magnetic field gradients, proton radiographs
are projection images of the MHD current along the proton trajectories. We demonstrate that in the linear
approximation, the full structure of the perpedicular magnetic field can be reconstructed by solving a steadystate inhomogeneous 2-dimensional diffusion equation sourced by the radiograph fluence contrast data. We explore limitations of the inversion method due to Poisson noise, to discretization errors, to radiograph edge effects, and to obstruction by laser target structures. We also provide a separate analysis that is well-suited to the inference of isotropic-homogeneous magnetic turbulence spectra. We discuss extension of these results to the nonlinear contrast regime.
description: PSFC REPORT PSFC/JA-16-67<br><br>This work was supported in part at the University of Chicago by the U.S. Department of Energy (DOE) under contract B523820 to the NNSA ASC/Alliances Center for Astrophysical Thermonuclear Flashes; the Office of Advanced Scientific Computing Research, Office of Science, U.S. DOE, under contract DE-AC02-06CH11357; the U.S. DOE NNSA ASC through the Argonne Institute for Computing in Science under field work proposal 57789; and the U.S. National Science Foundation under grant PHY-0903997.
DOI: 10.7910/dvn/dfastn
title: severe sputum sample 1 (paired end R2)

description: 16S rRNA gene sequencing data for Taiwanese male with severe COPD sputum sample 1. This was sequenced using Illumina MiSeq. This is the paired end R2 file for severe 1.
DOI: 10.7910/dvn/faezio
title: Baidu Mobility Data

description: The new dataset is updated to from September 23, 2021, to April 21, 2022. The historical Baidu mobility index data is to May 3, 2020 and Baidu stops updating after May 8, 2020. The data is scraped from Baidu Migration website. Before using the data, please check README.txt file in the dataset.
DOI: 10.5281/zenodo.16902603
title: Data set and Jupyter files to train a machine learning model to predict the hydrogen bond energy based on partial charges, bond orders, bond distances and element types

description: The capabilities of machine learning approaches was investigated to predict the hydrogen bond energy based on partial charges, bond orders, bond distances and element types. Support vector regression in combination with gradient boosting resulted in a mean absolute percentage error of 3 % which is a significant improvement compared to previous models. The best models include Löwdin partial charges and bond orders from BLYP or B3LYP with the def2-SVP double-ζ basis set. All models were fitted on coupled cluster energies with singles, doubles and perturbative triples extrapolated to the complete basis set limit.  

The data set contains the quantum chemistry data to train the models as well as the Jupyter notebooks. More information can be found in README.txt.
DOI: 10.15468/dl.qr9z8r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Vulpia bromoides",
 "Year 1000-2022"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011795-260120142942310/datasets/export for details.
DOI: 10.15468/dl.xmvm6q
title: Occurrence Download

description: A dataset containing 208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Geometry POLYGON((105 20,170 20,170 70,105 70,105 20))",
 "OccurrenceStatus is Present",
 "TaxonKey is Botrychium lanceolatum"
 ]
}

 The dataset includes 208 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011792-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/eyme9n
title: Taxonomy of the genus Aetheomorpha Lacordaire (Coleoptera: Chrysomelidae: Cryptocephalinae: Clytrini) from China, with description of five new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Wang, Feng-Yan, Zhou, Hong-Zhang (2012): Taxonomy of the genus Aetheomorpha Lacordaire (Coleoptera: Chrysomelidae: Cryptocephalinae: Clytrini) from China, with description of five new species. Journal of Natural History 46 (23 - 24): 1407-1440, DOI: 10.1080/00222933.2012.673642, URL: http://dx.doi.org/10.1080/00222933.2012.673642
DOI: 10.15468/ksscdv
title: Notes on Megalotomus Fieber, 1860 in the Palaearctic Region (Hemiptera, Heteroptera, Alydidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Yi, Wenbo, Wang, Shijun, Zhang, Hufang, Bu, Wenjun (2022): Notes on Megalotomus Fieber, 1860 in the Palaearctic Region (Hemiptera, Heteroptera, Alydidae). Zootaxa 5128 (2): 211-224, DOI: 10.11646/zootaxa.5128.2.3AbstractThe Palaearctic species of the genus Megalotomus Fieber, 1860 are reviewed. The diagnoses and distributions of Megalotomus species are updated. Megalotomus angulus (Hsiao, 1965) comb. nov. is transferred from Alydus Fabricius, 1803 and re-described based on type material from China. Megalotomus obtusus Ghauri, 1972 syn. nov. is proposed as a new junior synonym of M. angulus (Hsiao, 1965) comb. nov. based on examination of type material from Pakistan. Photographs of adults and illustrations of male external genitalia are provided. A review of their distribution and a key for their identification are provided.
DOI: 10.15468/dl.kf2yjf
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (China, Hong Kong)",
 "Geometry POLYGON((93.33723 19.22868,129.66277 19.22868,129.66277 33.77132,93.33723 33.77132,93.33723 19.22868))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Castanopsis eyrei"
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011794-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14621399
title: Contrastive Language Image Pretraining by openai
DOI: 10.15468/dl.m9w4xw
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is Saudi Arabia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Maerua crassifolia"
 ]
}

 The dataset includes 27 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011797-260120142942310/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.27854809
title: Estimation and Inference for Extreme Continuous Treatment Effects

description: This article studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE) and extreme average treatment effect (EATE), which are, respectively, the ratios of the quantile and tail mean at different treatment statuses. We propose estimators for the EQTE and EATE based on tail approximations from the extreme value theory. Our limiting theory is for the EQTE and EATE processes indexed by a set of quantile levels and pairs of different treatment statuses. It facilitates uniform inference for the EQTE and EATE over multiple tail levels and multiple treatment effects. Simulations suggest that our method works well in finite samples, and an empirical study illustrates its practical merits.
DOI: 10.6084/m9.figshare.27854809.v1
title: Estimation and Inference for Extreme Continuous Treatment Effects*

description: This paper studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE) and extreme average treatment effect (EATE), which are, respectively, the ratios of the quantile and tail mean at different treatment statuses. We propose estimators for the EQTE and EATE based on tail approximations from the extreme value theory. Our limiting theory is for the EQTE and EATE processes indexed by a set of quantile levels and pairs of different treatment statuses. It facilitates uniform inference for the EQTE and EATE over multiple tail levels and multiple treatment effects. Simulations suggest that our method works well in finite samples, and an empirical study illustrates its practical merits.
DOI: 10.6084/m9.figshare.27854809.v2
title: Estimation and Inference for Extreme Continuous Treatment Effects

description: This article studies estimation and inference for the treatment effect in deep tails of the potential outcome distributions corresponding to a continuously valued treatment, namely the extreme continuous treatment effect. We consider two measures for the tail characteristics: the quantile function and the tail mean function defined as the conditional mean beyond a quantile level. Then, for a quantile level close to 1, we define the extreme quantile treatment effect (EQTE) and extreme average treatment effect (EATE), which are, respectively, the ratios of the quantile and tail mean at different treatment statuses. We propose estimators for the EQTE and EATE based on tail approximations from the extreme value theory. Our limiting theory is for the EQTE and EATE processes indexed by a set of quantile levels and pairs of different treatment statuses. It facilitates uniform inference for the EQTE and EATE over multiple tail levels and multiple treatment effects. Simulations suggest that our method works well in finite samples, and an empirical study illustrates its practical merits.
DOI: 10.6084/m9.figshare.27854810
title: Rerandomization and Covariate Adjustment in Split-Plot Designs

description: Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the unit and group levels, allowing for consistent inference of average treatment effects. However, chance covariate imbalance is common in specific allocations and subjects subsequent inference to possibly large variability and conditional bias. Building on the literature on using rerandomization to improve covariate balance and inference efficiency in other design types, we propose two strategies for conducting rerandomization in split-plot designs and establish their guaranteed efficiency gains for inferring average treatment effects by the Horvitz–Thompson and Hajek estimators. We then propose two covariate adjustment methods that further improve inference efficiency when combined with rerandomization. All theoretical guarantees are design-based and robust to model misspecification.
DOI: 10.6084/m9.figshare.27854810.v2
title: Rerandomization and Covariate Adjustment in Split-Plot Designs

description: Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the unit and group levels, allowing for consistent inference of average treatment effects. However, chance covariate imbalance is common in specific allocations and subjects subsequent inference to possibly large variability and conditional bias. Building on the literature on using rerandomization to improve covariate balance and inference efficiency in other design types, we propose two strategies for conducting rerandomization in split-plot designs and establish their guaranteed efficiency gains for inferring average treatment effects by the Horvitz–Thompson and Hajek estimators. We then propose two covariate adjustment methods that further improve inference efficiency when combined with rerandomization. All theoretical guarantees are design-based and robust to model misspecification.
DOI: 10.6084/m9.figshare.27854810.v1
title: Rerandomization and covariate adjustment in split-plot designs

description: Split-plot designs are widely used in agricultural, industrial, social, and biomedical experiments to accommodate hard-to-change factors. Given multiple factors of interest and experimental units that are nested within groups, the design assigns a subset of the factors by a cluster randomization at the group level and assigns the remaining factors by a stratified randomization at the unit level. The randomization mechanism ensures covariate balance on average at both the unit and group levels, allowing for consistent inference of average treatment effects. However, chance covariate imbalance is common in specific allocations and subjects subsequent inference to possibly large variability and conditional bias. Building on the literature on using rerandomization to improve covariate balance and inference efficiency in other design types, we propose two strategies for conducting rerandomization in split-plot designs and establish their guaranteed efficiency gains for inferring average treatment effects by the Horvitz–Thompson and Hajek estimators. We then propose two covariate adjustment methods that further improve inference efficiency when combined with rerandomization. All theoretical guarantees are design-based and robust to model misspecification.
DOI: 10.6084/m9.figshare.28399994
title: A Nonparametric Bayesian Estimator of Copula Density with Applications to Financial Market

description: We propose a nonparametric Bayesian estimator of copula density based on the Logistic Gaussian Process density estimation method. A Gaussian process prior with flexible mean and covariance functions is placed on the underlying latent function. To avoid the typical boundary issues in copula density estimation, a transformation approach is adopted, allowing the latent process to be defined on an unrestricted support and then back-transformed to obtain the posterior copula density. We also develop a sampler to facilitate efficient sampling from the posterior copula distribution. Monte Carlo simulations demonstrate the strong overall and tail performance of the proposed estimator and the efficiency of the posterior sampler. We illustrate the usefulness of the proposed estimator through applications in financial dependence analysis, risk management, and option pricing.
DOI: 10.6084/m9.figshare.28399994.v1
title: A Nonparametric Bayesian Estimator of Copula Density with Applications to Financial Market

description: We propose a nonparametric Bayesian estimator of copula density based on the Logistic Gaussian Process density estimation method. A Gaussian process prior with flexible mean and covariance functions is placed on the underlying latent function. To avoid the typical boundary issues in copula density estimation, a transformation approach is adopted, allowing the latent process to be defined on an unrestricted support and then back-transformed to obtain the posterior copula density. We also develop a sampler to facilitate efficient sampling from the posterior copula distribution. Monte Carlo simulations demonstrate the strong overall and tail performance of the proposed estimator and the efficiency of the posterior sampler. We illustrate the usefulness of the proposed estimator through applications in financial dependence analysis, risk management, and option pricing.
DOI: 10.6084/m9.figshare.31147890
title: Temporal behavior of major crops with SWOT Ka-band SAR observations

description: Accurate monitoring of crop development is critical for understanding production drivers and implementing effective agricultural policies. In this study, we propose a novel application of the surface water and ocean topography (SWOT) Ka-band synthetic aperture radar (SAR) sensor by exploring the temporal evolution of its backscattering signature across various winter and summer crops. The analysis assessed the incidence angle dependency of SWOT backscatter for each crop type, the relationship between backscatter dynamics and soil moisture variation, and the influence of the parcel slope on near-nadir SWOT backscattering. Finally, the study provides a comprehensive evaluation of the temporal backscatter behavior of SWOT at key phenological stages of each crop compared to that obtained from the C-band Sentinel-1 time series. The results revealed a typical SAR incidence-angle dependency in SWOT for each crop type despite having a small range of incidence angles between 0.7° and 4°. With near vertical looking, SWOT backscattering dynamics followed the soil moisture variation regardless of vegetation attenuation. For near-nadir geometric acquisition, steeper slopes generally resulted in lower backscatters, except for wheat, barley, and grassland, which showed increased backscatters for steeper slopes during peak vegetation stages. Concerning SWOT temporal behavior, gramineous crops exhibited similar Ka- and C-band patterns from sowing to postgermination until the heading phase. The temporal profiles of maize and rapeseed in the Ka-band aligned with previously observed X-band behaviors, whereas sunflower showed a Ka-band backscattering pattern comparable to that of the C-band. With its 21-day revisit cycle, the SWOT Ka-band time series demonstrates promising potential for operational crop growth monitoring.
DOI: 10.6084/m9.figshare.31147890.v1
title: Temporal behavior of major crops with SWOT Ka-band SAR observations

description: Accurate monitoring of crop development is critical for understanding production drivers and implementing effective agricultural policies. In this study, we propose a novel application of the surface water and ocean topography (SWOT) Ka-band synthetic aperture radar (SAR) sensor by exploring the temporal evolution of its backscattering signature across various winter and summer crops. The analysis assessed the incidence angle dependency of SWOT backscatter for each crop type, the relationship between backscatter dynamics and soil moisture variation, and the influence of the parcel slope on near-nadir SWOT backscattering. Finally, the study provides a comprehensive evaluation of the temporal backscatter behavior of SWOT at key phenological stages of each crop compared to that obtained from the C-band Sentinel-1 time series. The results revealed a typical SAR incidence-angle dependency in SWOT for each crop type despite having a small range of incidence angles between 0.7° and 4°. With near vertical looking, SWOT backscattering dynamics followed the soil moisture variation regardless of vegetation attenuation. For near-nadir geometric acquisition, steeper slopes generally resulted in lower backscatters, except for wheat, barley, and grassland, which showed increased backscatters for steeper slopes during peak vegetation stages. Concerning SWOT temporal behavior, gramineous crops exhibited similar Ka- and C-band patterns from sowing to postgermination until the heading phase. The temporal profiles of maize and rapeseed in the Ka-band aligned with previously observed X-band behaviors, whereas sunflower showed a Ka-band backscattering pattern comparable to that of the C-band. With its 21-day revisit cycle, the SWOT Ka-band time series demonstrates promising potential for operational crop growth monitoring.
DOI: 10.6084/m9.figshare.25134272
title: The Block-Correlated Pseudo Marginal Sampler for State Space Models

description: Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.