cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1413845.10fu
title: SH1413845.10FU

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/sh1413846.10fu
title: SH1413846.10FU

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/sh1413850.10fu
title: SH1413850.10FU

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/sh1413853.10fu
title: SH1413853.10FU

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/sh1413856.10fu
title: SH1413856.10FU

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/sh1413858.10fu
title: SH1413858.10FU

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/sh1413859.10fu
title: SH1413859.10FU

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/sh1413865.10fu
title: SH1413865.10FU

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/sh1413870.10fu
title: SH1413870.10FU

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/sh1413876.10fu
title: SH1413876.10FU

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: 10.5281/zenodo.10933912
title: Dataset: Vocal complexity in the long calls of Bornean orangutans

description: Data and code used for PeerJ article titled, "Vocal complexity in the long calls of Bornean orangutans"

 

Abstract

Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable “long call” vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We also additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. We found low inter-observer reliability and poor classification accuracy using Ssupervised approaches, showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse type classification scheme approach that is highly reproducible across observers and exhibits high strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and measure quantifythe gradedness of in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires.
DOI: 10.5281/zenodo.10933913
title: Dataset: Vocal complexity in the long calls of Bornean orangutans

description: Data and code used for PeerJ article titled, "Vocal complexity in the long calls of Bornean orangutans"

 

Abstract

Vocal complexity is central to many evolutionary hypotheses about animal communication. Yet, quantifying and comparing complexity remains a challenge, particularly when vocal types are highly graded. Male Bornean orangutans (Pongo pygmaeus wurmbii) produce complex and variable “long call” vocalizations comprising multiple sound types that vary within and among individuals. Previous studies described six distinct call (or pulse) types within these complex vocalizations, but none quantified their discreteness or the ability of human observers to reliably classify them. We studied the long calls of 13 individuals to: 1) evaluate and quantify the reliability of audio-visual classification by three well-trained observers, 2) distinguish among call types using supervised classification and unsupervised clustering, and 3) compare the performance of different feature sets. Using 46 acoustic features, we used machine learning (i.e., support vector machines, affinity propagation, and fuzzy c-means) to identify call types and assess their discreteness. We also additionally used Uniform Manifold Approximation and Projection (UMAP) to visualize the separation of pulses using both extracted features and spectrogram representations. We found low inter-observer reliability and poor classification accuracy using Ssupervised approaches, showed low inter-observer reliability and poor classification accuracy, indicating that pulse types were not discrete. We propose an updated pulse type classification scheme approach that is highly reproducible across observers and exhibits high strong classification accuracy using support vector machines. Although the low number of call types suggests long calls are fairly simple, the continuous gradation of sounds seems to greatly boost the complexity of this system. This work responds to calls for more quantitative research to define call types and measure quantifythe gradedness of in animal vocal systems and highlights the need for a more comprehensive framework for studying vocal complexity vis-à-vis graded repertoires.
DOI: 10.60712/si-id54665.1
title: StrainInfo SI-ID 54665.1

description: StrainInfo dataset 54665 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54666.1
title: StrainInfo SI-ID 54666.1

description: StrainInfo dataset 54666 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54667.1
title: StrainInfo SI-ID 54667.1

description: StrainInfo dataset 54667 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54668.1
title: StrainInfo SI-ID 54668.1

description: StrainInfo dataset 54668 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54528.1
title: StrainInfo SI-ID 54528.1

description: StrainInfo dataset 54528 about a strain of Haemophilus parainfluenzae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.15468/dl.ierm8s
title: Occurrence Download

description: A dataset containing 12393 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Poa chaixii Vill.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 12393 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001057-160623103540712/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.60712/si-id54529.1
title: StrainInfo SI-ID 54529.1

description: StrainInfo dataset 54529 about a strain of Haemophilus haemolyticus. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54530.1
title: StrainInfo SI-ID 54530.1

description: StrainInfo dataset 54530 about a strain of Haemophilus parainfluenzae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54531.1
title: StrainInfo SI-ID 54531.1

description: StrainInfo dataset 54531 about a strain of Haemophilus parainfluenzae. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54617.1
title: StrainInfo SI-ID 54617.1

description: StrainInfo dataset 54617 about a strain of Hafnia alvei. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.5281/zenodo.11549183
title: Dataset: Technological attribute classification of lithics from Middle and Late Mesolithic sites in western and central Norway

description: The repository contains data and codes from technological attribute classification of lithic blade assemblages from 29 Middle and Late Mesolithic settlement sites (dated to c. 7500-5000 BC) in western and central Norway. The data was collected by the research project “The Storegga tsunami c. 6150 BC - a wave of destruction and the start of something new?”, funded by The Research Council of Norway (FRIPRO-project no. 302858).

The data and coding supports the findings of the paper "Lithic technology before and after the Storegga tsunami (8200 cal BP) - dissolving large-scale regional trends to identify social impact of crisis in western Norway" by H. Damlien, A.J. Nyland and J. Redmond, submitted to The Holocene.

The file contains four sheets:



Protocol: desciptions of selected attributes.

Coding.

Data from attribute analysis.

References.
DOI: 10.5281/zenodo.11549184
title: Dataset: Technological attribute classification of lithics from Middle and Late Mesolithic sites in western and central Norway

description: The repository contains data and codes from technological attribute classification of lithic blade assemblages from 29 Middle and Late Mesolithic settlement sites (dated to c. 7500-5000 BC) in western and central Norway. The data was collected by the research project “The Storegga tsunami c. 6150 BC - a wave of destruction and the start of something new?”, funded by The Research Council of Norway (FRIPRO-project no. 302858).

The data and coding supports the findings of the paper "Lithic technology before and after the Storegga tsunami (8200 cal BP) - dissolving large-scale regional trends to identify social impact of crisis in western Norway" by H. Damlien, A.J. Nyland and J. Redmond, submitted to The Holocene.

The file contains four sheets:



Protocol: desciptions of selected attributes.

Coding.

Data from attribute analysis.

References.
DOI: 10.60712/si-id54618.1
title: StrainInfo SI-ID 54618.1

description: StrainInfo dataset 54618 about a strain of Hafnia alvei. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54619.1
title: StrainInfo SI-ID 54619.1

description: StrainInfo dataset 54619 about a strain of Hafnia alvei. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54620.1
title: StrainInfo SI-ID 54620.1

description: StrainInfo dataset 54620 about a strain of Hafnia alvei. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54669.1
title: StrainInfo SI-ID 54669.1

description: StrainInfo dataset 54669 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54670.1
title: StrainInfo SI-ID 54670.1

description: StrainInfo dataset 54670 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.
DOI: 10.60712/si-id54671.1
title: StrainInfo SI-ID 54671.1

description: StrainInfo dataset 54671 about a strain of Helicobacter pylori. StrainInfo is a service developed to provide a resolution of microbial strain identifiers by storing culture collection numbers, their relations, and culture-associated data. StrainInfo is part of NFDI4Microbiota consortium.