cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.swhaqb
title: Occurrence Download

description: A dataset containing 14003253 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Living Specimen, Human Observation)",
 "Country is Portugal",
 "IucnRedListCategory is one of (CR, EN, VU, NT, LC)"
 ]
}

 The dataset includes 14003253 records from 387 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008104-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14990433
title: Teacher motivation in higher education institutions: a bibliometric approach
DOI: 10.5281/zenodo.14990434
title: Teacher motivation in higher education institutions: a bibliometric approach
DOI: 10.5066/p13m7w4g
title: Genomic data for rabbitbrush and big sagebrush plant communities in western North America (2017 - 2019) and Fendler’s globemallow in New Mexico (2022)

description: These data were compiled to highlight hybridization mosaics and genotype-environment associations across three study systems containing taxa critical to widespread plant communities in western North America: Artemisia tridentata, Ericameria nauseosa, and Sphaeralcea fendleri. Objective(s) of our study were to: (1) use genomic data to identify divergent evolutionary lineages (hereafter parental taxa) and hybrids among them, (2) assess correlations between allele frequencies and environmental gradients that may be indicative of adaptation and use machine learning algorithms to predict ancestry based on the environment for each lineage (i.e., parental taxa and hybrids), and (3) leverage relationships between environmental characteristics and ancestry to project parental and hybrid climate envelopes across geographic space and quantify the ecological ramification of hybridization for each study system in both present and future climates. These data represent reduced representation genomic data for multiple populations and individuals across western North America for Artemisia tridentata, Ericameria nauseosa, and Sphaeralcea fendleri. These data were collected in or created for Artemisia tridentata and Ericameria nauseosa in 2017-2019 across the entirety of the range of these systems in western North America and for Sphaeralcea fendleri in 2022 in New Mexico, USA. These data were collected by the University of Nevada, Reno, US Forest Service (Rocky Mountain Research Station), Bureau of Land Management, and the U.S. Geological Survey. A couple leaves from a single plant were collected and identified in the field by experts. These data can be used in further landscape genetic studies within these systems in this region. Either can be subset to particular regions or added to projects.
DOI: 10.15468/dl.29fgm4
title: Occurrence Download

description: A dataset containing 1568 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Eriogonum davidsonii Greene"
 ]
}

 The dataset includes 1568 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008123-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5519/qd.8gzakcce
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (139 records)
DOI: 10.15468/dl.taumxv
title: Occurrence Download

description: A dataset containing 355 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gallinago imperialis P.L.Sclater & Salvin, 1869"
 ]
}

 The dataset includes 355 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008199-250227182430271/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.9117125
title: Comparative Analysis of Gut Microbiota in Collegiate Swimmers Following Changes in Exercise

description: Exercise can
influence gut microbial community structure and diversity; however, the
temporal dynamics of this association have rarely been explored. Here we
characterized fecal microbiota in response to short term changes in training
volume. Fecal samples, body composition, and training logs were collected from
Division I NCAA collegiate swimmers during peak training through their
in-season taper in 2016 (n = 9) and 2017 (n = 7), capturing a systematic
reduction in training volume near the conclusion of their athletic season.
Fecal microbiota were characterized using 16S rRNA V4 amplicon sequencing and
multivariate statistical analysis, Spearman rank correlations, and random
forest models. Peak training volume, measured as swimming distance, decreased
significantly during the study period from 32.6 ± 4.8 km/wk to 11.3 ± 8.1 km/wk
(ANOVA, p &lt; 0.05); however, body composition showed no significant
changes. Coinciding with the decrease
in training volume, the microbial community structure showed a significant decrease
in overall microbial diversity, a decrease in microbial community structural
similarity, and a decrease in the proportion of the bacterial genera <i>Faecalibacterium</i> and <i>Coprococcu</i>s. Together these data
suggested changes in training volume significantly influence the gut
microbiome; future research will establish whether these changes are associated
with energy balance or nutrient intake.
DOI: 10.15468/dl.7cfnke
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Ptilotrichum rupestre (Ten.) Boiss., Koniga rupestris (Ten.) Heynh., Aurinia rupestris (Ten.) Cullen & T.R.Dudley, Anodontea rupestris Sweet, Phyllolepidum rupestre (Sweet) Trinajstić)",
 "Year 1960-2025"
 ]
}

 The dataset includes 8 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008200-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8bfk4j
title: Occurrence Download

description: A dataset containing 8206 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Minytrema melanops (Rafinesque, 1820)"
 ]
}

 The dataset includes 8206 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008201-250227182430271/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2gwp24
title: CCDC 2290716: Experimental Crystal Structure Determination

description: Related Article: Magdalena Grzelak, Dharmendra Kumar, Michał Andrzej Kochman, Maja Morawiak, Gabriela Wiosna-Sałyga, Adam Kubas, Przemysław Data, Marcin Lindner|2024|Chemical Science|15|8404|doi:10.1039/D4SC01391C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.14990477
title: ammi_trifoliatum_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14990478
title: ammi_trifoliatum_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.q34txx
title: Occurrence Download

description: A dataset containing 77886 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is one of (Brazil, Argentina, Paraguay)",
 "TaxonKey is Pionus maximiliani (Kuhl, 1820)",
 "Year 1000-2024"
 ]
}

 The dataset includes 77886 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008118-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14990487
title: ampelosycios_bosseri_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14990488
title: ampelosycios_bosseri_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.nbpwk4
title: Occurrence Download

description: A dataset containing 82719 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Galeopsis bifida Boenn.)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1945"
 ]
}

 The dataset includes 82719 records from 391 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008122-250227182400228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b7tddx
title: Occurrence Download

description: A dataset containing 25902800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Brazil",
 "HasCoordinate is true",
 "TaxonKey is Animalia",
 "Year 1900-2025"
 ]
}

 The dataset includes 25902800 records from 1953 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0058477-250920141307145/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.29039883.v1
title: Finite Mixtures of Multivariate Contaminated Normal Censored Regression Models

description: The complexity of model-based clustering grows as outliers become more prevalent, compounded by restrictions imposed by the detection of quantification. This article introduces a finite mixture of multivariate contaminated normal (FM-MCN) distributions tailored for handling censored data, referred to as the FM-MCNC model henceforth. Subsequently, the FM-MCNC model is extended to tackle the multivariate linear regression issue, leading to the formulation of the FM-MCN censored regression (FM-MCNCR) model. For the estimation of model parameters, we devise a computationally analytical alternating expectation conditional maximization (AECM) algorithm. Additionally, we present an information matrix-based formula to approximate the asymptotic standard errors of parameter estimates. Importantly, the AECM algorithm serves a dual role by not only facilitating parameter estimation but also providing methods to recover censored measurements and detect outlier data points as a by-product when it converges. The efficacy and advantages of the proposed methodology are illustrated through a series of simulations and two real-life data examples. Supplemental data for this article can be accessed here.
DOI: 10.6084/m9.figshare.29039883
title: Finite Mixtures of Multivariate Contaminated Normal Censored Regression Models

description: The complexity of model-based clustering grows as outliers become more prevalent, compounded by restrictions imposed by the detection of quantification. This article introduces a finite mixture of multivariate contaminated normal (FM-MCN) distributions tailored for handling censored data, referred to as the FM-MCNC model henceforth. Subsequently, the FM-MCNC model is extended to tackle the multivariate linear regression issue, leading to the formulation of the FM-MCN censored regression (FM-MCNCR) model. For the estimation of model parameters, we devise a computationally analytical alternating expectation conditional maximization (AECM) algorithm. Additionally, we present an information matrix-based formula to approximate the asymptotic standard errors of parameter estimates. Importantly, the AECM algorithm serves a dual role by not only facilitating parameter estimation but also providing methods to recover censored measurements and detect outlier data points as a by-product when it converges. The efficacy and advantages of the proposed methodology are illustrated through a series of simulations and two real-life data examples. Supplemental data for this article can be accessed here.
DOI: 10.15156/bio/sh1056590.10fu
title: SH1056590.10FU

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/sh1056591.10fu
title: SH1056591.10FU

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/sh1056593.10fu
title: SH1056593.10FU

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/sh1056596.10fu
title: SH1056596.10FU

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/sh1056598.10fu
title: SH1056598.10FU

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/sh1056599.10fu
title: SH1056599.10FU

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/sh1056600.10fu
title: SH1056600.10FU

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/sh1056601.10fu
title: SH1056601.10FU

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/sh1056602.10fu
title: SH1056602.10FU

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/sh1056608.10fu
title: SH1056608.10FU

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.