cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1196109.10fu
title: SH1196109.10FU

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/sh1196114.10fu
title: SH1196114.10FU

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/sh1196115.10fu
title: SH1196115.10FU

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/sh1196116.10fu
title: SH1196116.10FU

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/sh1196118.10fu
title: SH1196118.10FU

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/sh1196119.10fu
title: SH1196119.10FU

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.bvdg39
title: Occurrence Download

description: A dataset containing 4005 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zaprora silenus Jordan, 1896",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 4005 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055022-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60895/redata/fduncn
title: Bladder cancer cell lines amyloid proteome

description: Despite the current diagnostic and therapeutic approaches to bladder cancer being widely accepted, there have been few significant advancements in this field over the past decades. This underscores the necessity for a paradigm shift in the approach to bladder cancer. The role of amyloids in cancer remains unclear despite their identification in several other pathologies. In this study, we present evidence of amyloids in bladder cancer, both in vitro and in vivo. In a murine model of bladder cancer, a positive correlation was observed between amyloids and tumor stage, indicating an association between amyloids and bladder cancer progression. Subsequently, the amyloid proteome of the RT4 non-invasive and HT1197 invasive bladder cancer cell lines was identified and included oncogenes, tumor suppressors, and highly expressed cancer-related proteins. It is proposed that amyloids function as structures that sequester key proteins. Therefore, amyloids should be considered in the study and diagnosis of bladder cancer.
DOI: 10.15468/dl.29y9n9
title: Occurrence Download

description: A dataset containing 298 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zapteryx exasperata (Jordan & Gilbert, 1880)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 298 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055023-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gp748t
title: Occurrence Download

description: A dataset containing 10664 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghum halepense (L.) Pers.",
 "Geometry POLYGON((-109 26, -87 26, -87 44, -109 44, -109 26))"
 ]
}

 The dataset includes 10664 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055024-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4mc3ch
title: Occurrence Download

description: A dataset containing 34020 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amblyomma americanum Linnaeus, 1758"
 ]
}

 The dataset includes 34020 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055025-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pmgef3
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Congo",
 "Geometry POLYGON((14.07175 1.34445,15.48333 1.34445,15.48333 2.34259,14.07175 2.34259,14.07175 1.34445))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 69 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055028-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3qdsw2
title: Occurrence Download

description: A dataset containing 1048 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zenion hololepis (Goode & Bean, 1896)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1048 records from 39 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055026-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zz2cpk
title: Occurrence Download

description: A dataset containing 46103 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is one of (Northeast Fisheries Science Center Bottom Trawl Survey Data, Maritimes Spring Research Vessel Surveys, Maine Department of Marine Resources Inshore Trawl Survey 2000-2019, Maritimes Summer Research Vessel Surveys, DFO Quebec Region Magdalen Islands Lobster Survey, Northeast Area Monitoring and Assessment Program Near Shore Trawl Survey (NEAMAP), iNaturalist Research-grade Observations, ECNASAP - East Coast North America Strategic Assessment)",
 "TaxonKey is Homarus americanus H.Milne Edwards, 1837",
 "Year 1986-2025"
 ]
}

 The dataset includes 46103 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055027-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fjqfeb
title: Occurrence Download

description: A dataset containing 2226 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zenopsis conchifer (Lowe, 1852)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 2226 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055029-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnrzgv
title: Occurrence Download

description: A dataset containing 8078 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Sorghastrum nutans (L.) Nash",
 "Geometry POLYGON((-109 26, -87 26, -87 44, -109 44, -109 26))"
 ]
}

 The dataset includes 8078 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055031-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f7dny4
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zesticelus profundorum (Gilbert, 1896)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 29 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055032-241126133413365/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.28124693
title: Molecular Genetic Assessment Aids in Clarifying the Phylogenetic Status of Iranian Kerman Wild Sheep

description: Two species of wild sheep inhabit Iran: Asiatic mouflon (Ovis gmelini) and urial (O. vignei). Phylogenetic relationships between populations distributed in this country are complex, and still remain unclear. This study aimed to clarify, by genetic assessment, the phylogenetic status of Kerman wild sheep, considered to be a hybrid of the two species. For this purpose, we created a dataset that included specimens of O. gmelini, O. vignei and Kerman sheep. We applied genome-wide SNP genotyping technology to analyze population structure and genetic diversity of these groups. It was demonstrated that Kerman sheep differentiated from other groups and occupied an intermediate position between O. gmelini and O. vignei on Neighbor-Net and PCA plots. Using Admixture analysis, two ancestral components were identified in this population; however, admixed ancestry was not confirmed by f3 statistics. Genetic diversity in Kerman wild sheep was significantly higher than in any group of O. vignei, but lower than in O. gmelini. Additionally, we examined complete mitochondrial genomes and it was demonstrated that the matrilineal ancestor of Kerman sheep belonged to O. vignei. Our results lead to the conclusion that Kerman wild sheep can be recognized as a separate subspecies of O. vignei.
DOI: 10.15468/dl.b894qt
title: Occurrence Download

description: A dataset containing 1099 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Africa",
 "Country is Congo",
 "Geometry POLYGON((14.07175 1.34445,15.48333 1.34445,15.48333 2.34259,14.07175 2.34259,14.07175 1.34445))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Animalia"
 ]
}

 The dataset includes 1099 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055033-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r8m8cx
title: Occurrence Download

description: A dataset containing 1701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zeugopterus punctatus (Bloch, 1787)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 1701 records from 84 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055034-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p32tj2
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Some finds of insects in autumn 2021 in Kharkiv region, Ukraine"
 ]
}

 The dataset includes 157 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055035-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14589504
title: sicyos_bulbosus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14589505
title: sicyos_bulbosus_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14589497
title: Swift/BAT-GUANO upper limits map for S241230bd
DOI: 10.5281/zenodo.14589498
title: Swift/BAT-GUANO upper limits map for S241230bd
DOI: 10.5281/zenodo.14589487
title: Records of defecation times of 8 individuals throughout the year 2024

description: 8 individuals aged between 20 and 30, of mixed biological sex, self-reported their defecation times from January 1st, 2024, to December 31st, 2024. Some entries might be time shifted of +- 24 hours due to reporting failures. As this was mostly due to forgotten defecations, back-to-back reports with less than 5 minutes between eachother were separated by time-shifting the second report 24 hours back in time. This means that the data is more accurate when considered with a daily or larger granularity. No other metadata was collected from participants during this time period.The dataset (poop.csv) is a Comma-Separated Values file (RFC 4180) contains the following columns, with one row per event:



date: Central European Standard time of defecation, following ISO 8601 specifications. The timestamp is day-specific for what regards daylight saving times. This means that the reported time is what wall-clocks reported when the defecation occurred.

unix_time: UNIX timestamp of defecation (in seconds).

author: unique string with reporter identifier of that defecation. Author identifiers are pseudonimized.


All partecipants have agreed to publish the data in this form.
DOI: 10.5281/zenodo.14589488
title: Records of defecation times of 8 individuals throughout the year 2024

description: 8 individuals aged between 20 and 30, of mixed biological sex, self-reported their defecation times from January 1st, 2024, to December 31st, 2024. Some entries might be time shifted of +- 24 hours due to reporting failures. As this was mostly due to forgotten defecations, back-to-back reports with less than 5 minutes between eachother were separated by time-shifting the second report 24 hours back in time. This means that the data is more accurate when considered with a daily or larger granularity. No other metadata was collected from participants during this time period.The dataset (poop.csv) is a Comma-Separated Values file (RFC 4180) contains the following columns, with one row per event:



date: Central European Standard time of defecation, following ISO 8601 specifications. The timestamp is day-specific for what regards daylight saving times. This means that the reported time is what wall-clocks reported when the defecation occurred.

unix_time: UNIX timestamp of defecation (in seconds).

author: unique string with reporter identifier of that defecation. Author identifiers are pseudonimized.


All partecipants have agreed to publish the data in this form.
DOI: 10.5281/zenodo.14589519
title: sicyos_chiriquensis_decontaminated_FSCR.fasta.gz
DOI: 10.5281/zenodo.14589520
title: sicyos_chiriquensis_decontaminated_FSCR.fasta.gz
DOI: 10.15468/dl.zfhzjs
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "Geometry POLYGON((-76.0827 -55.49165,-66.70549 -55.49165,-66.70549 -17.43931,-76.0827 -17.43931,-76.0827 -55.49165))",
 "HasGeospatialIssue is false",
 "TaxonKey is Triatoma infestans (Klug, 1834)",
 "Year 1985-2025"
 ]
}

 The dataset includes 15 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0055037-241126133413365/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.27850081
title: Sparse Model-Based Clustering of Three-Way Data via Lasso-Type Penalties

description: Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are increasingly common in various fields. Despite their widespread use and successful applications, these models suffer from over-parameterization, making them not suitable for even moderately sized matrix-variate data. To address this issue, we introduce a sparse model-based clustering approach for three-way data. Our approach assumes that the matrix mixture parameters are sparse and have different degrees of sparsity across clusters, enabling the induction of parsimony in a flexible manner. Estimation relies on the maximization of a penalized likelihood, with specifically tailored group and graphical lasso penalties. These penalties facilitate the selection of the most informative features for clustering three-way data where variables are recorded over multiple occasions, as well as allowing the identification of cluster-specific association structures. We conduct extensive testing of the proposed methodology on synthetic data and validate its effectiveness through an application to time-dependent crime patterns across multiple U.S. cities. Supplementary files for this article are available online.
DOI: 10.6084/m9.figshare.27850081.v2
title: Sparse Model-Based Clustering of Three-Way Data via Lasso-Type Penalties

description: Mixtures of matrix Gaussian distributions provide a probabilistic framework for clustering continuous matrix-variate data, which are increasingly common in various fields. Despite their widespread use and successful applications, these models suffer from over-parameterization, making them not suitable for even moderately sized matrix-variate data. To address this issue, we introduce a sparse model-based clustering approach for three-way data. Our approach assumes that the matrix mixture parameters are sparse and have different degrees of sparsity across clusters, enabling the induction of parsimony in a flexible manner. Estimation relies on the maximization of a penalized likelihood, with specifically tailored group and graphical lasso penalties. These penalties facilitate the selection of the most informative features for clustering three-way data where variables are recorded over multiple occasions, as well as allowing the identification of cluster-specific association structures. We conduct extensive testing of the proposed methodology on synthetic data and validate its effectiveness through an application to time-dependent crime patterns across multiple U.S. cities. Supplementary files for this article are available online.