cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1165427.10fu
title: SH1165427.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1165429.10fu
title: SH1165429.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1165432.10fu
title: SH1165432.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1165433.10fu
title: SH1165433.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.3uf3ce
title: Occurrence Download

description: A dataset containing 5798 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is North America",
 "TaxonKey is Helianthus annuus L.",
 "Year 1000-2021"
 ]
}

 The dataset includes 5798 records from 205 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044628-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7275/ir-55258
title: Databases of invasive plant geography in the conterminous United States

description: Non-native plants have the potential to harm ecosystems. Harm is classically related to their distribution and abundance, but this geographical information is often unknown. Here, we assess geographical commonness as a potential indicator of invasive status for non-native flora in the United States. Geographical commonness could inform invasion risk assessments across species and ecoregions. We compiled occurrence and abundance data from 14 spatial datasets and used this information to categorize non-native species as uncommon or common based on three dimensions of commonness: area of occupancy, habitat breadth, and local abundance. To assess consistency in invasive plant categorizations, we compared commonness to invasive status in the United States. We identified species with higher-than-expected abundance relative to their occupancy, habitat breadth, or residence time. We calculated non-native plant richness within United States ecoregions and estimated unreported species based on rarefaction/extrapolation curves.
DOI: 10.6084/m9.figshare.28074533
title: QEC_data.zip

description: This repository contains error correction simulation data presented in Ref.[1].Data sets correspond to k = 2, 3, 4 <i>La-cross</i> quantum LDPC codes under hardware-specific and agnostic noise models (see Ref.[1]).[1] Laura Pecorari, Sven Jandura, Gavin K. Brennen, and Guido Pupillo, "High-rate quantum LDPC codes for long-range-connected neutral atom registers", arXiv:2404.13010 (2024)
DOI: 10.6084/m9.figshare.28074533.v1
title: QEC_data.zip

description: This repository contains error correction simulation data presented in Ref.[1].Data sets correspond to k = 2, 3, 4 <i>La-cross</i> quantum LDPC codes under hardware-specific and agnostic noise models (see Ref.[1]).[1] Laura Pecorari, Sven Jandura, Gavin K. Brennen, and Guido Pupillo, "High-rate quantum LDPC codes for long-range-connected neutral atom registers", arXiv:2404.13010 (2024)
DOI: 10.60520/ieda/113521
title: Palau temporal seawater isotope, precipitation isotope, and salinity data

description: Palau precipitation isotope, seawater isotope and salinity data from Koror, Palau from 2013 to 2023.
DOI: 10.15468/dl.hgexwt
title: Occurrence Download

description: A dataset containing 71858 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((-5.98969 37.43568,-6.42812 36.72615,-5.55125 36.03026,-4.22342 36.71611,-5.41346 37.69385,-5.98969 37.43568))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 71858 records from 134 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044629-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u45uux
title: Occurrence Download

description: A dataset containing 530468 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-85.81262 -35.55544,-24.47364 -35.55544,-24.47364 13.82393,-85.81262 13.82393,-85.81262 -35.55544))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Columba livia J.F.Gmelin, 1789"
 ]
}

 The dataset includes 530468 records from 117 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0044618-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5066/p96q1ozg
title: Repeat microgravity data from Phoenix, Arizona, 2020-2023

description: This dataset represents the network-adjusted results of relative- and absolute-gravity surveys performed from 2020 to 2023 in and near the city of Phoenix, Arizona. Relative surveys were done using a Zero Length Spring, Inc. Burris relative-gravity meter. Absolute-gravity surveys were done using a Micro-g LaCoste, Inc. A-10 absolute-gravity meter. The effect of solid Earth tides and ocean loading were removed from the data prior to network adjustment. Non-linear instrument drift was removed by evaluating gravity change during repeated measurements at one or more base stations. Vertical gradients between the relative- and absolute-gravity meters were measured at each station where both types of measurement were collected to correlate the measurements of the two instruments. Vertical gradients were measured using a relative-gravity meter and tripod set to the height of the absolute-gravity meter. Relative-gravity differences and absolute-gravity data were combined using least-squares network adjustment, as implemented in the software GSadjust (https://code.usgs.gov/sgp/gsadjust). Data are provided for 105 stations collected over 4 discrete, annual surveys.
DOI: 10.6073/pasta/733eb5f8a42df9004e3b34e806486aec
title: PAR data for the Green Lake 4 buoy, 2018 - ongoing.

description: High-resolution water quality data are fundamental to observing
 rapid ecological responses to meteorology, climate, and other
 disturbance events. Here we describe the deployment of a single buoy
 line with multiple sensors at fixed-depths from a subsurface float
 in the water-column of Green Lake 4 (GL4). Sensors on the buoy
 collect data in both summer and winter, thereby providing valuable
 insights into lake characteristics beyond our standard sampling
 period, including key transitional periods such as ice formation and
 ice break-up.
DOI: 10.6084/m9.figshare.27396562
title: Multi-Task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

description: Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27396562.v1
title: Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

description: Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients.
DOI: 10.6084/m9.figshare.27396562.v2
title: Multi-Task Learning for Gaussian Graphical Regressions with High Dimensional Covariates

description: Gaussian graphical regression is a powerful approach for regressing the precision matrix of a Gaussian graphical model on covariates, which permits the response variables and covariates to outnumber the sample size. However, traditional approaches of fitting the model via separate node-wise lasso regressions overlook the network-induced structure among these regressions, leading to high error rates, particularly when the number of nodes is large. To address this issue, we propose a multi-task learning estimator for fitting Gaussian graphical regression models, which incorporates a cross-task group sparsity penalty and a within-task element-wise sparsity penalty to govern the sparsity of active covariates and their effects on the graph, respectively. We also develop an efficient augmented Lagrangian algorithm for computation, which solves subproblems with a semi-smooth Newton method. We further prove that our multi-task learning estimator has considerably lower error rates than the separate node-wise regression estimates, as the cross-task penalty enables borrowing information across tasks. We examine the utility of our method through simulations and an application to a gene co-expression network study with brain cancer patients. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27188817
title: Efficient Modeling of Spatial Extremes over Large Geographical Domains

description: Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined though a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse probabilistic structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27188817.v1
title: Efficient Modeling of Spatial Extremes over Large Geographical Domains

description: Various natural phenomena exhibit spatial extremal dependence at short spatial distances. However, existing models proposed in the spatial extremes literature often assume that extremal dependence persists across the entire domain. This is a strong limitation when modeling extremes over large geographical domains, and yet it has been mostly overlooked in the literature. We here develop a more realistic Bayesian framework based on a novel Gaussian scale mixture model, with the Gaussian process component defined though a stochastic partial differential equation yielding a sparse precision matrix, and the random scale component modeled as a low-rank Pareto-tailed or Weibull-tailed spatial process determined by compactly-supported basis functions. We show that our proposed model is approximately tail-stationary and that it can capture a wide range of extremal dependence structures. Its inherently sparse probabilistic structure allows fast Bayesian computations in high spatial dimensions based on a customized Markov chain Monte Carlo algorithm prioritizing calibration in the tail. We fit our model to analyze heavy monsoon rainfall data in Bangladesh. Our study shows that our model outperforms natural competitors and that it fits precipitation extremes well. We finally use the fitted model to draw inference on long-term return levels for marginal precipitation and spatial aggregates. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27317143
title: Heterogeneous Functional Regression for Subgroup Analysis

description: With ever increasing number of features of modern datasets, data heterogeneity is gradually becoming the norm rather than the exception. Whereas classical regressions usually assume all the samples follow a common model, it becomes imperative to identify the heterogeneous relationship in different subsamples. In this article, we propose a new approach to model heterogeneous functional regression relations. We target at the association between a response and a predictor, whose relationship can vary across underlying subgroups and is modeled as an unknown functional of an auxiliary predictor. We introduce a procedure which performs simultaneous parameter estimation and subgroup identification through a fusion type group-wise penalization. We establish the statistical guarantees in terms of non-asymptotic convergence of the parameter estimation. We also establish the oracle property and asymptotic normality of the estimators. We carry out intensive simulations, and illustrate with a new dataset from an Alzheimer’s disease study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27317143.v2
title: Heterogeneous Functional Regression for Subgroup Analysis

description: With ever increasing number of features of modern datasets, data heterogeneity is gradually becoming the norm rather than the exception. Whereas classical regressions usually assume all the samples follow a common model, it becomes imperative to identify the heterogeneous relationship in different subsamples. In this article, we propose a new approach to model heterogeneous functional regression relations. We target at the association between a response and a predictor, whose relationship can vary across underlying subgroups and is modeled as an unknown functional of an auxiliary predictor. We introduce a procedure which performs simultaneous parameter estimation and subgroup identification through a fusion type group-wise penalization. We establish the statistical guarantees in terms of non-asymptotic convergence of the parameter estimation. We also establish the oracle property and asymptotic normality of the estimators. We carry out intensive simulations, and illustrate with a new dataset from an Alzheimer’s disease study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.27317143.v1
title: Heterogeneous functional regression for subgroup analysis

description: With ever increasing number of features of modern datasets, data heterogeneity is gradually becoming the norm rather than the exception. Whereas classical regressions usually assume all the samples follow a common model, it becomes imperative to identify the heterogeneous relationship in different subsamples. In this article, we propose a new approach to model heterogeneous functional regression relations. We target at the association between a response and a predictor, whose relationship can vary across underlying subgroups and is modeled as an unknown functional of an auxiliary predictor. We introduce a procedure which performs simultaneous parameter estimation and subgroup identification through a fusion type group-wise penalization. We establish the statistical guarantees in terms of non-asymptotic convergence of the parameter estimation. We also establish the oracle property and asymptotic normality of the estimators. We carry out intensive simulations, and illustrate with a new dataset from an Alzheimer’s disease study.
DOI: 10.6084/m9.figshare.28074647
title: Brachy mcc data

description: Raw data associated with manuscript "Mate-choice copying in the livebearing fish <i>Brachyrhaphis rhabdophora</i>"
DOI: 10.6084/m9.figshare.28074647.v1
title: Brachy mcc data

description: Raw data associated with manuscript "Mate-choice copying in the livebearing fish <i>Brachyrhaphis rhabdophora</i>"
DOI: 10.5281/zenodo.14537926
title: Protein assemblies design models

description: Design model PDBs of protein assemblies reported in the manuscript "Bond-centric modular design of protein assemblies": doi: https://doi.org/10.1101/2024.10.11.617872.
DOI: 10.5281/zenodo.14537927
title: Protein assemblies design models

description: Design model PDBs of protein assemblies reported in the manuscript "Bond-centric modular design of protein assemblies": doi: https://doi.org/10.1101/2024.10.11.617872.
DOI: 10.5281/zenodo.7563401
title: Unveiling the role of chromosome structure morphology on gene function through chromosome conformation analysis

description: The genome populations of H1-hESC and HFFc6 generated by the Integrated Genome Modeling Platform (IGM) (https://github.com/alberlab/igm).
DOI: 10.5281/zenodo.7563402
title: Unveiling the role of chromosome structure morphology on gene function through chromosome conformation analysis

description: The genome populations of H1-hESC and HFFc6 generated by the Integrated Genome Modeling Platform (IGM) (https://github.com/alberlab/igm).
DOI: 10.5066/p1nn2qym
title: Geospatial characterization of Atlantic-facing New Jersey salt marshes

description: This data release contains coastal wetland synthesis products for the Atlantic-facing New Jersey salt marshes. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services.