cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0180833.10fu
title: SH0180833.10FU

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/sh0180834.10fu
title: SH0180834.10FU

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/sh0180835.10fu
title: SH0180835.10FU

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/sh0180836.10fu
title: SH0180836.10FU

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/sh0180837.10fu
title: SH0180837.10FU

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/sh0180838.10fu
title: SH0180838.10FU

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/sh0180839.10fu
title: SH0180839.10FU

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/sh0180840.10fu
title: SH0180840.10FU

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: 10.21227/r9q2-8036
title: RACTC

description: In this work, we study urban dynamics at the census tract level.Information on census tracts and their corresponding geographic boundaries is available through the US Census Bureau survey. Each predetermined census tract is treated as a region. We use the GPS coordinates to compute the distance between two region nodes.We then extract the geospatial data including categories of points of interest and road networks from the OpenStreetMap platform, and use the POI category information as regions' nominal attributes.
DOI: 10.15468/dl.k3dmhg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Beilschmiedia angustielliptica Lorea-Hern.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015917-250127130748423/datasets/export for details.
DOI: 10.15468/dl.mz52n2
title: Occurrence Download

description: A dataset containing 569 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-92.97748 14.39123,-82.19994 7.11948,-77.45586 6.78943,-76.87266 9.06894,-81.23629 10.59056,-83.53187 15.85056,-85.31008 16.29981,-87.56482 16.3396,-87.39508 17.52797,-88.26111 18.71166,-89.409 18.0841,-91.12063 18.25247,-91.47072 16.56496,-91.94744 16.26676,-92.63401 14.71862,-92.97748 14.39123))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Laccaria Berk. & Broome, 1883",
 "Year 1000-2025"
 ]
}

 The dataset includes 569 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015918-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wjtp4h
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Trichoferus campestris (Faldermann, 1835)"
 ]
}

 The dataset includes 27 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015919-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d6gxg4
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Beilschmiedia hondurensis Kosterm.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015921-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b55jf5
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.16_1",
 "TaxonKey is Onychomys leucogaster (Wied-Neuwied, 1841)"
 ]
}

 The dataset includes 27 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0029997-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gj8avn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Beilschmiedia manantlanensis Cuevas & Cochrane)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015922-250127130748423/datasets/export for details.
DOI: 10.6084/m9.figshare.28057256
title: Estimation and Variable Selection for Interval-Censored Failure Time Data with Random Change Point and Application to Breast Cancer Study

description: Motivated by a breast cancer study, we consider regression analysis of interval-censored failure time data in the presence of a random change point. Although a great deal of literature on interval-censored data has been established, there does not seem to exist an established method that can allow for the existence of random change points. Such data can occur in, for example, clinical trials where the risk of a disease may dramatically change when some biological indexes of the human body exceed certain thresholds. To fill the gap, we will first consider regression analysis of such data under a class of linear transformation models and provide a sieve maximum likelihood estimation procedure. Then a penalized method is proposed for simultaneous estimation and variable selection, and the asymptotic properties of the proposed method are established. An extensive simulation study is conducted and indicates that the proposed methods work well in practical situations. The approaches are applied to the real data from the breast cancer study mentioned above. 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.28057256.v1
title: Estimation and Variable Selection for Interval-censored Failure Time Data with Random Change Point and Application to Breast Cancer Study

description: Motivated by a breast cancer study, we consider regression analysis of interval-censored failure time data in the presence of a random change point. Although a great deal of literature on interval-censored data has been established, it does not seem to exist an established method that can allow for the existence of random change points. Such data can occur in, for example, clinical trials where the risk of a disease may dramatically change when some biological indexes of the human body exceed certain thresholds. To fill the gap, we will first consider regression analysis of such data under a class of linear transformation models and provide a sieve maximum likelihood estimation procedure. Then a penalized method is proposed for simultaneous estimation and variable selection, and the asymptotic properties of the proposed method are established. An extensive simulation study is conducted and indicates that the proposed methods work well in practical situations. The approaches are applied to the real data from the breast cancer study mentioned above.
DOI: 10.6084/m9.figshare.28087123
title: Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings

description: Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free comparisons but lack granularity; ratings provide cardinal and granular assessments but may be highly subjective or inconsistent. Collecting and analyzing rankings and ratings jointly has not been performed until recently due to a lack of principled methods. In this work, we propose a flexible, joint statistical model for rankings and ratings—the Bradley-Terry-Luce-Binomial (BTL-Binomial). The model captures rater effects and preference heterogeneity, respectively, with judge-specific random effects and a latent class mixture framework where the number of classes is unknown a priori. We propose computationally-efficient estimation via a Bayesian mixture of finite mixtures (MFM) approach. Finally, we demonstrate statistical inference and decision-making based on rankings and ratings jointly through applications to real and simulated datasets in academic peer review. 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.28087123.v1
title: Modeling Preferences: A Bayesian Mixture of Finite Mixtures for Rankings and Ratings

description: Rankings and ratings are commonly used to express preferences but provide distinct and complementary information. Rankings give ordinal and scale-free comparisons but lack granularity; ratings provide cardinal and granular assessments but may be highly subjective or inconsistent. Collecting and analyzing rankings and ratings jointly has not been performed until recently due to a lack of principled methods. In this work, we propose a flexible, joint statistical model for rankings and ratings—the Bradley-Terry-Luce-Binomial (BTL-Binomial). The model captures rater effects and preference heterogeneity, respectively, with judge-specific random effects and a latent class mixture framework where the number of classes is unknown a priori. We propose computationally-efficient estimation via a Bayesian mixture of finite mixtures (MFM) approach. Finally, we demonstrate statistical inference and decision-making based on rankings and ratings jointly through applications to real and simulated datasets in academic peer review. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.8xkjdn
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Beilschmiedia mexicana (Mez) Kosterm.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015924-250127130748423/datasets/export for details.
DOI: 10.15468/dl.fbnazd
title: Occurrence Download

description: A dataset containing 52459 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Myrica adenophora Hance, Myrica arborea Hutch., Myrica cacuminis Britton & P.Wilson, Myrica californica Cham. & Schltdl., Myrica caroliniensis Mill., Myrica cerifera Bigelow, Myrica chevalieri (Verd. & Polhill) Christenh. & Byng, Myrica esculenta Buch.-Ham. ex D.Don, Myrica faya (Dryand.) Aiton, Myrica funckii A.Chev., Myrica holdridgeana Lundell, Myrica inodora W.Bartram, Myrica integra (A.Chev.) Killick, Myrica javanica Reinw. ex Blume, Myrica kandtiana Engl., Myrica lindeniana C.DC., Myrica parvifolia Benth., Myrica pavonis C.DC., Morella Lour., Myrica picardae Krug & Urb., Myrica pilulifera Rendle, Myrica pringlei Greenm., Myrica pubescens Humb. & Bonpl. ex Willd., Myrica punctata Griseb., Myrica rotundata Steyerm. & Maguire, Myrica rubra A.Chev., Morella Lour., Myrica serrata Penhallow, 1907, Myrica shaferi Urb. & Britton, Myrica singularis Parra-Os., Myrica spathulata Baill.)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 52459 records from 536 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015923-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2k6esg
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Beilschmiedia ovalioides Sach.Nishida)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015925-250127130748423/datasets/export for details.
DOI: 10.6084/m9.figshare.28352018
title: Actitudes hacia las matemáticas y su relación con el rendimiento académico: validación de un instrumento en el contexto ecuatoriano.

description: El presente estudio tuvo como objetivo adaptar y validar un instrumento de medición de las actitudes hacia las matemáticas en estudiantes de educación secundaria del contexto ecuatoriano, considerando la importancia de evaluar estas actitudes para mejorar la enseñanza de esta disciplina. Se llevó a cabo un estudio cuantitativo, descriptivo y correlacional, con una población de 24,711 estudiantes de bachillerato, de la cual se extrajo una muestra probabilística de 6,078 estudiantes mediante un muestreo estratificado, representativa en términos de género y autodefinición étnica. El proceso incluyó la evaluación de la fiabilidad mediante el coeficiente alfa de Cronbach (0.829), análisis de multicolinealidad, análisis factorial exploratorio (AFE), análisis factorial confirmatorio (AFC) y un análisis de invarianza de medición para verificar la equivalencia entre los grupos. Los índices de ajuste (NFI, RFI, IFI, TLI y CFI) fueron todos superiores a 0.9, y el RMSEA fue de 0.056 (IC: 0.053-0.059). Los resultados confirmaron la validez y fiabilidad del instrumento adaptado, reduciendo los factores de cinco a tres: Apreciación, Ansiedad, y Autoconfianza y Valoración. Las conclusiones subrayan la utilidad de este cuestionario para medir actitudes hacia las matemáticas en el contexto ecuatoriano, proporcionando una herramienta válida y fiable para futuras investigaciones y prácticas educativas.
DOI: 10.15468/dl.4p4nun
title: Occurrence Download

description: A dataset containing 3863781 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OrderKey is Odonata",
 "Country is one of (Norway, Guernsey, Poland, Denmark, Kosovo, Latvia, France, Slovakia, Romania, Bulgaria, Croatia, Iceland, Ireland, Malta, Åland Islands, North Macedonia, Andorra, Montenegro, Germany, Belarus, Czechia, United Kingdom of Great Britain and Northern Ireland, Switzerland, Belgium, Gibraltar, Portugal, Monaco, Ukraine, Finland, Bosnia and Herzegovina, Hungary, Estonia, Greece, Serbia, Netherlands, Moldova, Republic of, Cyprus, Isle of Man, Lithuania, Slovenia, Albania, Sweden, Liechtenstein, Austria, Italy, Luxembourg, Spain, Jersey)",
 "HasCoordinate is TRUE",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "BasisOfRecord is one of (Living Specimen, Observation, Human Observation, Machine Observation, Occurrence evidence)"
 ]
}

 The dataset includes 3863781 records from 1384 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015894-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.