cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1376839.10fu
title: SH1376839.10FU

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/sh1376842.10fu
title: SH1376842.10FU

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/sh1376843.10fu
title: SH1376843.10FU

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/sh1376845.10fu
title: SH1376845.10FU

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/sh1376847.10fu
title: SH1376847.10FU

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/sh1376848.10fu
title: SH1376848.10FU

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/sh1376853.10fu
title: SH1376853.10FU

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/sh1376855.10fu
title: SH1376855.10FU

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/sh1376856.10fu
title: SH1376856.10FU

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/sh1376858.10fu
title: SH1376858.10FU

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.ed8vbs
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Distichium inclinatum",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 128 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023938-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18269476
title: Classified TLS and SLAM Point Clouds for Semantic Analysis in Built Environments – Uniud Rizzi Campus (Courtyard)

description: This dataset, entitled “Uniud Rizzi Campus (Courtyard)”, contains semantically classified three-dimensional point clouds acquired in an outdoor built environment and processed using different mapping technologies. It was created to support comparative analyses of semantic classification performance on point clouds generated by Terrestrial Laser Scanning (TLS) and by multiple Simultaneous Localization and Mapping (SLAM) algorithms.

The dataset includes one TLS-derived reference point cloud and four SLAM-derived point clouds reconstructed from the same raw mobile robotics mapping data using different SLAM approaches (RTAB-Map, LIO-SAM, DLO, and LeGO-LOAM). All point clouds are provided with manual semantic annotations and predefined training and test splits corresponding to those adopted in the associated experimental study. 

Detailed information on dataset structure and usage is provided in the accompanying README.md file.

When using this dataset, please cite the associated publication: Matellon, A., Maset, E., Beinat, A., & Visintini, D. (2026). How the Choice of SLAM Algorithm Impacts Point Cloud Classification: An Experimental Evaluation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-2/W12-2026, 295-302, https://doi.org/10.5194/isprs-archives-XLVIII-2-W12-2026-295-2026  

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

The point clouds included in this dataset are derived from the data acquisition campaign originally presented in: Tiozzo Fasiolo, D., Scalera, L., Maset, E., 2023. Comparing LiDAR and IMU-based SLAM approaches for 3D robotic mapping. Robotica, 41(9), 2588–2604, https://doi.org/10.1017/S026357472300053X 

While the original publication focused on the comparison of SLAM approaches for 3D mapping, the present dataset provides manually classified point clouds and predefined training and test splits, enabling further research on semantic classification and downstream analysis. Users interested in the raw sensor data are referred to the original publication and to the dataset available at: https://github.com/diegotiozzo21/mobile-robotics-uniud-datasets
DOI: 10.5281/zenodo.18269477
title: Classified TLS and SLAM Point Clouds for Semantic Analysis in Built Environments – Uniud Rizzi Campus (Courtyard)

description: This dataset, entitled “Uniud Rizzi Campus (Courtyard)”, contains semantically classified three-dimensional point clouds acquired in an outdoor built environment and processed using different mapping technologies. It was created to support comparative analyses of semantic classification performance on point clouds generated by Terrestrial Laser Scanning (TLS) and by multiple Simultaneous Localization and Mapping (SLAM) algorithms.

The dataset includes one TLS-derived reference point cloud and four SLAM-derived point clouds reconstructed from the same raw mobile robotics mapping data using different SLAM approaches (RTAB-Map, LIO-SAM, DLO, and LeGO-LOAM). All point clouds are provided with manual semantic annotations and predefined training and test splits corresponding to those adopted in the associated experimental study. 

Detailed information on dataset structure and usage is provided in the accompanying README.md file.

When using this dataset, please cite the associated publication: Matellon, A., Maset, E., Beinat, A., & Visintini, D. (2026). How the Choice of SLAM Algorithm Impacts Point Cloud Classification: An Experimental Evaluation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVIII-2/W12-2026, 295-302, https://doi.org/10.5194/isprs-archives-XLVIII-2-W12-2026-295-2026  

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

The point clouds included in this dataset are derived from the data acquisition campaign originally presented in: Tiozzo Fasiolo, D., Scalera, L., Maset, E., 2023. Comparing LiDAR and IMU-based SLAM approaches for 3D robotic mapping. Robotica, 41(9), 2588–2604, https://doi.org/10.1017/S026357472300053X 

While the original publication focused on the comparison of SLAM approaches for 3D mapping, the present dataset provides manually classified point clouds and predefined training and test splits, enabling further research on semantic classification and downstream analysis. Users interested in the raw sensor data are referred to the original publication and to the dataset available at: https://github.com/diegotiozzo21/mobile-robotics-uniud-datasets
DOI: 10.60797/irj.2026.164.18.1
title: Основные трудности адаптации

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.60797/irj.2026.164.18.2
title: Комплекс мер адаптации и повышению уровня знаний о российской культуре

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.60797/irj.2026.164.17.2
title: Сценарий тестирования блока модели «Емкость»

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.60797/irj.2026.164.19.2
title: Согласованность результатов ИФА между тест-системами ARRIAH и IZSLER при исследовании образцов сыворотки крови КРС, не вакцинированного против ящура

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.60797/irj.2026.164.17.4
title: Классификация дефектов

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.60797/irj.2026.164.21.2
title: Клинико-лабораторные характеристики пациентов с IgEзависимыми заболеваниями

description: Международный научно-исследовательский журнал, Выпуск 2 (164) 2026
DOI: 10.15468/dl.7m5jfj
title: Occurrence Download

description: A dataset containing 173 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is Geodetic datum assumed WGS84",
 "OccurrenceStatus is Present",
 "TaxonKey is Lithobates spectabilis (Hillis & Frost, 1985)",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 173 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0277603-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rewtb8
title: Occurrence Download

description: A dataset containing 953 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cinchona L."
 ]
}

 The dataset includes 953 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0279081-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9x56ey
title: Occurrence Download

description: A dataset containing 492 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Evarcha jucunda (Lucas, 1846)",
 "Year is greater than 2013",
 "Geometry MULTIPOLYGON (((19.51 47.46,19.51 36.21,6.41 36.21,6.41 47.46,19.51 47.46)))"
 ]
}

 The dataset includes 492 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036776-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tzf6ea
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ornithodoros porcinus domesticus Walton, 1962"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286069-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wkhher
title: Occurrence Download

description: A dataset containing 3204 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hippocampus reidi Ginsburg, 1933"
 ]
}

 The dataset includes 3204 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0279086-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dh9fj9
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Pliocercus wilmarai Smith, Perez-Higareda & Chiszar, 1996"
 ]
}

 The dataset includes 15 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0277607-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pns5re
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Eswatini",
 "InstitutionCode is ciat"
 ]
}

 The dataset includes 102 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286066-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.axx8c9
title: Occurrence Download

description: A dataset containing 65 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lactuca setosa Stebbins ex C.Jeffrey"
 ]
}

 The dataset includes 65 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002714-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8mqmq5
title: Occurrence Download

description: A dataset containing 70229 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "DatasetKey is NHMD Ichthyology Collection",
 "InstitutionKey is 68fcaba0-61fe-4e78-a460-4f37654d025d"
 ]
}

 The dataset includes 70229 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286015-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9vb8f
title: Occurrence Download

description: A dataset containing 2214 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Echinocactus platyacanthus Link & Otto"
 ]
}

 The dataset includes 2214 records from 74 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0277611-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k4s3wr
title: Occurrence Download

description: A dataset containing 373 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Heliocybe sulcata (Berk.) Redhead & Ginns"
 ]
}

 The dataset includes 373 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0279089-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.