cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh2003970.08fu
title: SH2003970.08FU

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/sh1324883.08fu
title: SH1324883.08FU

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/sh1324884.08fu
title: SH1324884.08FU

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/sh2003971.08fu
title: SH2003971.08FU

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/sh2003972.08fu
title: SH2003972.08FU

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/sh1324885.08fu
title: SH1324885.08FU

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/sh1324886.08fu
title: SH1324886.08FU

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: 10.6084/m9.figshare.25267245
title: An Interpretable Neural Network-based Nonproportional Odds Model for Ordinal Regression

description: This study proposes an interpretable neural network-based nonproportional odds model (N<sup>3</sup>POM) for ordinal regression. N<sup>3</sup>POM is different from conventional approaches to ordinal regression with nonproportional models in several ways: (a) N<sup>3</sup>POM is defined for both continuous and discrete responses, whereas standard methods typically treat the continuous variables as if they were discrete, (b) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a nonlinear neural network to serve as a coefficient function. Thanks to the neural network, N<sup>3</sup>POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N<sup>3</sup>POM to several real-world datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25267245.v2
title: An Interpretable Neural Network-based Nonproportional Odds Model for Ordinal Regression

description: This study proposes an interpretable neural network-based nonproportional odds model (N<sup>3</sup>POM) for ordinal regression. N<sup>3</sup>POM is different from conventional approaches to ordinal regression with nonproportional models in several ways: (a) N<sup>3</sup>POM is defined for both continuous and discrete responses, whereas standard methods typically treat the continuous variables as if they were discrete, (b) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a nonlinear neural network to serve as a coefficient function. Thanks to the neural network, N<sup>3</sup>POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N<sup>3</sup>POM to several real-world datasets. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25267245.v1
title: An interpretable neural network-based non-proportional odds model for ordinal regression

description: This study proposes an interpretable neural network-based non-proportional odds model (N3POM) for ordinal regression. N3POM is different from conventional approaches to ordinal regression with non-proportional models in several ways: (1) N3POM is defined for both continuous and discrete responses, whereas standard methods typically treat the continuous variables as if they were discrete, (2) instead of estimating response-dependent finite-dimensional coefficients of linear models from discrete responses as is done in conventional approaches, we train a non-linear neural network to serve as a coefficient function. Thanks to the neural network, N3POM offers flexibility while preserving the interpretability of conventional ordinal regression. We establish a sufficient condition under which the predicted conditional cumulative probability locally satisfies the monotonicity constraint over a user-specified region in the covariate space. Additionally, we provide a monotonicity-preserving stochastic (MPS) algorithm for effectively training the neural network. We apply N3POM to several real-world datasets.
DOI: 10.6084/m9.figshare.25222259
title: Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness

description: Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint. We propose a fast and scalable estimation algorithm that achieves sublinear convergence, and the upper bound for the estimation error of the proposed method is rigorously derived. Experimental results support our theoretical claims, and the proposed estimator shows its merits compared to other existing methods. The proposed method is applied to analyze the National Health and Nutrition Examination Survey data. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.25222259.v1
title: Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness*

description: Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint. We propose a fast and scalable estimation algorithm that achieves sublinear convergence, and the upper bound for the estimation error of the proposed method is rigorously derived. Experimental results support our theoretical claims, and the proposed estimator shows its merits compared to other existing methods. The proposed method is applied to analyze the National Health and Nutrition Examination Survey data. Supplementary materialsfor this article are available online.
DOI: 10.6084/m9.figshare.25222259.v2
title: Mixed Matrix Completion in Complex Survey Sampling under Heterogeneous Missingness

description: Modern surveys with large sample sizes and growing mixed-type questionnaires require robust and scalable analysis methods. In this work, we consider recovering a mixed dataframe matrix, obtained by complex survey sampling, with entries following different canonical exponential distributions and subject to heterogeneous missingness. To tackle this challenging task, we propose a two-stage procedure: in the first stage, we model the entry-wise missing mechanism by logistic regression, and in the second stage, we complete the target parameter matrix by maximizing a weighted log-likelihood with a low-rank constraint. We propose a fast and scalable estimation algorithm that achieves sublinear convergence, and the upper bound for the estimation error of the proposed method is rigorously derived. Experimental results support our theoretical claims, and the proposed estimator shows its merits compared to other existing methods. The proposed method is applied to analyze the National Health and Nutrition Examination Survey data. Supplementary materials for this article are available online.
DOI: 10.15468/dl.ys73mf
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mucuna reticulata Burck"
 ]
}

 The dataset includes 30 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056323-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m9nsga
title: Occurrence Download

description: A dataset containing 743 species occurrences available in GBIF matching the query:
{
 "and" : [
 "InstitutionKey is 67392d7a-a48d-439b-a9dc-f6e0e7d85846",
 "TaxonKey is one of (Squaliformes)"
 ]
}

 The dataset includes 743 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056324-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8vv4b5
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Medinilla sessiliflora Regalado"
 ]
}

 The dataset includes 22 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056325-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bmaqed
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Memecylon mananjebense H.Perrier"
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056326-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dsh5kv
title: Occurrence Download

description: A dataset containing 332 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Minuartia funkii (Jord.) Graebn."
 ]
}

 The dataset includes 332 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056327-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5zy3wf
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microstegium fauriei (Hayata) Honda"
 ]
}

 The dataset includes 41 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056328-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xqrsdz
title: Occurrence Download

description: A dataset containing 2096 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Estonia, Russian Federation, Germany, Poland, Belarus, Ukraine, Hungary)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dicranum polysetum Sw."
 ]
}

 The dataset includes 2096 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056329-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aneb7v
title: Occurrence Download

description: A dataset containing 56 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mucuna revoluta Wilmot-Dear"
 ]
}

 The dataset includes 56 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056331-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q4n2yp
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Memecylon mandrarense H.Perrier"
 ]
}

 The dataset includes 27 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056332-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2qtyc8
title: Occurrence Download

description: A dataset containing 275 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Minuartia globulosa (Labill.) Schinz & Thell."
 ]
}

 The dataset includes 275 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056334-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.76pyc9
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microstegium geniculatum (Hayata) Honda"
 ]
}

 The dataset includes 128 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056335-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4k46vk
title: Occurrence Download

description: A dataset containing 24 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Medinilla sessilifolia Merr."
 ]
}

 The dataset includes 24 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056333-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x86e4b
title: Occurrence Download

description: A dataset containing 49422 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is one of (Saint Vincent and the Grenadines, Grenada)"
 ]
}

 The dataset includes 49422 records from 392 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056322-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10893941
title: Quantification of ROIs corresponding to MQs from fluorescence in vivo imaging experiments

description: We injected DIr labelled macrophages into mice carring immunolgical hot and cold KPC pancreatic tumors and quantified the recruitment to the tumor sites and lungs of the injected cells at different days after injection using fluorescence imaging. We hypotesized that macrophages would be recruited into tumor tissue and in prevalence into cold tumors.
DOI: 10.5281/zenodo.10893942
title: Quantification of ROIs corresponding to MQs from fluorescence in vivo imaging experiments

description: We injected DIr labelled macrophages into mice carring immunolgical hot and cold KPC pancreatic tumors and quantified the recruitment to the tumor sites and lungs of the injected cells at different days after injection using fluorescence imaging. We hypotesized that macrophages would be recruited into tumor tissue and in prevalence into cold tumors.
DOI: 10.15468/dl.82rgjz
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mucuna sakapipei Wiriad."
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056336-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nt86x8
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Memecylon mangiferoides Jacq.-Fél."
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056337-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.