cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0166096.10fu
title: SH0166096.10FU

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/sh0166097.10fu
title: SH0166097.10FU

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: 10.17632/68rc9crgkj
title: A Hybrid Quantum-Classical Network Intrusion Detection Dataset

description: The increasing scale and complexity of modern networks have intensified the need for robust and generalizable network intrusion detection systems (NIDS). While machine learning–based security solutions have shown promise, their effectiveness is often constrained by limitations in existing benchmark datasets, including severe class imbalance, outdated attack representations, and the absence of security-aware features that reflect emerging cryptographic and communication paradigms. In parallel, advances in quantum technologies are reshaping the cybersecurity landscape. Quantum Key Distribution (QKD) introduces fundamentally new security properties such as measurement disturbance, quantum bit error rate (QBER), and eavesdropping detectability—that are not captured by classical network traffic features. However, the lack of publicly available datasets integrating quantum-inspired descriptors has hindered research into hybrid quantum-classical cybersecurity models.

To address this gap, this dataset presents HQC-2026 (Hybrid Quantum-Classical 2026), the first publicly available network intrusion detection dataset enriched with quantum-inspired features. The dataset was generated in a controlled cyber-range environment at the Military Institute of Science and Technology (MIST), Bangladesh, and contains 14.69 million samples with perfectly balanced classes, covering seven attack categories and benign traffic. HQC-2026 combines 89 classical network flow features with 7 quantum-inspired features derived from simulated BB84 QKD protocols, resulting in a 96-dimensional feature space. The dataset includes SMOTE-balanced labels and reproducible OpenQASM 2.0 quantum circuits for transparent quantum feature generation. HQC-2026 is intended to support research in intrusion detection, hybrid quantum-classical machine learning, and quantum-inspired cybersecurity, providing a scalable and reproducible benchmark for future studies.
DOI: 10.17632/68rc9crgkj.1
title: A Hybrid Quantum-Classical Network Intrusion Detection Dataset

description: The increasing scale and complexity of modern networks have intensified the need for robust and generalizable network intrusion detection systems (NIDS). While machine learning–based security solutions have shown promise, their effectiveness is often constrained by limitations in existing benchmark datasets, including severe class imbalance, outdated attack representations, and the absence of security-aware features that reflect emerging cryptographic and communication paradigms. In parallel, advances in quantum technologies are reshaping the cybersecurity landscape. Quantum Key Distribution (QKD) introduces fundamentally new security properties such as measurement disturbance, quantum bit error rate (QBER), and eavesdropping detectability—that are not captured by classical network traffic features. However, the lack of publicly available datasets integrating quantum-inspired descriptors has hindered research into hybrid quantum-classical cybersecurity models.

To address this gap, this dataset presents HQC-2026 (Hybrid Quantum-Classical 2026), the first publicly available network intrusion detection dataset enriched with quantum-inspired features. The dataset was generated in a controlled cyber-range environment at the Military Institute of Science and Technology (MIST), Bangladesh, and contains 14.69 million samples with perfectly balanced classes, covering seven attack categories and benign traffic. HQC-2026 combines 89 classical network flow features with 7 quantum-inspired features derived from simulated BB84 QKD protocols, resulting in a 96-dimensional feature space. The dataset includes SMOTE-balanced labels and reproducible OpenQASM 2.0 quantum circuits for transparent quantum feature generation. HQC-2026 is intended to support research in intrusion detection, hybrid quantum-classical machine learning, and quantum-inspired cybersecurity, providing a scalable and reproducible benchmark for future studies.
DOI: 10.15156/bio/sh0166098.10fu
title: SH0166098.10FU

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/sh0166099.10fu
title: SH0166099.10FU

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: 10.17632/pnx57t4vnc
title: Replication package for Koomen and Wicht (2026) "Granularity in the current account"

description: The file includes the replication package for Koomen and Wicht (2026) "Granularity in the current account".
DOI: 10.15156/bio/sh0166100.10fu
title: SH0166100.10FU

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/sh0166101.10fu
title: SH0166101.10FU

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: 10.17632/pnx57t4vnc.1
title: Replication package for Koomen and Wicht (2026) "Granularity in the current account"

description: The file includes the replication package for Koomen and Wicht (2026) "Granularity in the current account".
DOI: 10.15156/bio/sh0166102.10fu
title: SH0166102.10FU

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/sh0166103.10fu
title: SH0166103.10FU

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/sh0166104.10fu
title: SH0166104.10FU

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/sh0166105.10fu
title: SH0166105.10FU

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: 10.17632/pvrdx7hwhz
title: Toxicity Detection Dataset in Twi Language

description: This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:

COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.
LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.
Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).
Key Features:
• Total records: 2,001
• Language: Primarily Akan (Twi)
• Classification type: Binary toxicity classification
There are no missing values (both columns have 2,001 non-null entries)
Data types: ‘COMMENT’: string and ‘LABEL’`: string
This dataset can support research in:
• Toxic language detection in low-resource languages
• Natural Language Processing (NLP) for African languages
• Machine learning model training for text classification
• Sociolinguistic analysis of online conversational content
The File Format is CSV file: Toxicity_dataset.csv
It contains two columns: 'COMMENT' and ‘LABEL'
DOI: 10.17632/pvrdx7hwhz.2
title: Toxicity Detection Dataset in Twi Language

description: This dataset contains 2,001 text entries labeled for toxicity classification. Each entry represents a user-generated comment along with an assigned toxicity label. The dataset is structured into two columns:

COMMENT– A text field containing comments written primarily in Akan (Twi). These comments include expressions of gratitude, feedback, conversational messages, and general communication typical of social or online interactions.
LABEL– A categorical variable indicating whether the comment is 'toxic' or 'non-toxic'.
Current labels present in the dataset: 'non-toxic' (and any others present in the full file, if applicable).
Key Features:
• Total records: 2,001
• Language: Primarily Akan (Twi)
• Classification type: Binary toxicity classification
There are no missing values (both columns have 2,001 non-null entries)
Data types: ‘COMMENT’: string and ‘LABEL’`: string
This dataset can support research in:
• Toxic language detection in low-resource languages
• Natural Language Processing (NLP) for African languages
• Machine learning model training for text classification
• Sociolinguistic analysis of online conversational content
The File Format is CSV file: Toxicity_dataset.csv
It contains two columns: 'COMMENT' and ‘LABEL'
DOI: 10.15468/dl.maj9gt
title: Occurrence Download

description: A dataset containing 2221 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Morus nigra L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2221 records from 218 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0299260-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pjqa94
title: Occurrence Download

description: A dataset containing 6183 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ophrys tenthredinifera Willd.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6183 records from 82 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0298465-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pc67jq
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Silene salamandra Pamp.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 13 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0288472-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p7d7sb
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alchemilla wallischii Pawl.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0286824-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8zgvv2
title: Occurrence Download

description: A dataset containing 302 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Harrisia bonplandii (J.Parm. ex Pfeiff.) Britton & Rose)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 302 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0083947-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bvadg9
title: Occurrence Download

description: A dataset containing 78773 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arctium lappa L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 78773 records from 674 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0256640-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hbr6e7
title: Occurrence Download

description: A dataset containing 101455 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carex digitata L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 101455 records from 316 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0291563-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pkbr9k
title: Occurrence Download

description: A dataset containing 2859 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Campanula bononiensis L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2859 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0257533-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pcmch2
title: Occurrence Download

description: A dataset containing 5586 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Koenigia islandica L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5586 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0262912-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.avdyu7
title: Occurrence Download

description: A dataset containing 362156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Impatiens glandulifera Royle",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 362156 records from 553 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0265583-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.