cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1206384.10fu
title: SH1206384.10FU

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/sh1206385.10fu
title: SH1206385.10FU

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/sh1206386.10fu
title: SH1206386.10FU

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/sh1206388.10fu
title: SH1206388.10FU

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/sh1206393.10fu
title: SH1206393.10FU

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/sh1206397.10fu
title: SH1206397.10FU

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/sh1206400.10fu
title: SH1206400.10FU

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/sh1206401.10fu
title: SH1206401.10FU

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.aanqtc
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mauria obtusifolia"
 ]
}

 The dataset includes 13 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0080272-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15066649
title: HDMA bigwigs - Part 1

description: Bigwig tracks for each cell type in HDMA (Liu*, Jessa*, Kim*, Ng* et al, bioRxiv 2025). Includes the Macs2 p-value signal track for observed acessibility, the bias-corrected ChromBPNet-predicted accessibility, and base-resolution contribution scores. Predictions and contributions represent averages across folds per cell type. A detailed description of the main data types deposited on Zenodo can be found here.
DOI: 10.5281/zenodo.15066651
title: HDMA bigwigs - Part 1

description: Bigwig tracks for each cell type in HDMA (Liu*, Jessa*, Kim*, Ng* et al, bioRxiv 2025). Includes the Macs2 p-value signal track for observed acessibility, the bias-corrected ChromBPNet-predicted accessibility, and base-resolution contribution scores. Predictions and contributions represent averages across folds per cell type. A detailed description of the main data types deposited on Zenodo can be found here.
DOI: 10.15468/dl.l2q7tw
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035451-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jgkh76
title: Occurrence Download

description: A dataset containing 627 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Eccremocarpus scaber Ruiz & Pav., Eccremocarpus scaber var. carmineus Spigolatore, Eccremocarpus scaber var. carmineus Pynaert, 1905, Eccremocarpus scaber subsp. carmineus (Spigolatore) Pynaert, Eccremocarpus scaber var. sepium Bertero ex DC., Eccremocarpus scaber var. sepium Bertero, Eccremocarpus scaber var. scaber, Eccremocarpus scaber var. roseus Huxley, Eccremocarpus scaber var. aurea Benary)"
 ]
}

 The dataset includes 627 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071486-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mcdy1q
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000495-180824113759888/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dwdxoa
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010310-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yhtycw
title: Occurrence Download

description: A dataset containing 21672 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cola Schott & Endl."
 ]
}

 The dataset includes 21672 records from 295 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0260125-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.srhjrt
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0078560-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xft5it
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004231-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rk92wr
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Andaniopsis nordlandica (Boeck, 1871)"
 ]
}

 The dataset includes 112 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0112838-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yslngf
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012860-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sdwn7x
title: Occurrence Download

description: A dataset containing 32 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 32 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0035467-190918142434337/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.moiziy
title: Occurrence Download

description: A dataset containing 896052923 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 896052923 records from 16195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019878-180131172636756/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18464292
title: The win-win-win papakonstantinidis model

description: The Win-Win-Win Papakonstantinidis Model is a strategic bargaining and conflict resolution framework that expands the traditional "win-win" scenario to include a third, crucial stakeholder: the Community. It aims to achieve mutually beneficial outcomes for all three parties (e.g., businesses/individuals, other involved parties, and society/the environment) by integrating social responsibility, ethics, and empathy into decision-making processes. 

The model moves beyond pure economic rationality and competition to a more holistic approach that incorporates social motivation and collective welfare: 

•                      Tripartite Focus: It transforms two-party negotiations into a three-dimensional process, ensuring outcomes benefit "me," "you," and "the community".

•                      Empathy and Social Justice: The framework suggests that cooperation is driven by empathy and social trust, not just competition. It emphasizes the "sensitization process" where participants consider community norms and social justice.

•                      Beyond Pareto Efficiency: While traditional models (like Pareto optimality) focus on resource allocation where no one can be made better off without making another worse off, the Win-Win-Win model introduces the community (the "C" factor) to add quality elements like equality and justice, aiming for a higher "equi-harmony" point that maximizes the triple utility for all involved.

•                      Behavioral Methodologies: The model uses behavioral analysis to transform perceptions and encourage "active participation" and self-organization within communities, particularly in local development and governance decisions. 

Application of AI in the Model

Recent research has explored the integration of Artificial Intelligence (AI) with the Papakonstantinidis model to enhance community growth and social cohesion. 

•                      Data Integration and Participatory Tools: AI platforms can process large-scale community data to optimize resource allocation, enhance participatory governance, and mediate stakeholder involvement, thus addressing systemic inequalities.

•                      Empowering Social Economy Enterprises (SEEs): AI marketing platforms can help SEEs (which prioritize social and environmental sustainability over profit) engage with stakeholders more effectively, bridging the gap with established businesses and promoting local sustainable growth.

Predicting Behavior: Unlike the standard stakeholder model, the Win-Win-Win model can use AI-compatible quantitative foundations to predict the behavior of bargainers by modeling individual decision-making, which helps in designing better public health strategies or local policies
DOI: 10.5281/zenodo.18464293
title: The win-win-win papakonstantinidis model

description: The Win-Win-Win Papakonstantinidis Model is a strategic bargaining and conflict resolution framework that expands the traditional "win-win" scenario to include a third, crucial stakeholder: the Community. It aims to achieve mutually beneficial outcomes for all three parties (e.g., businesses/individuals, other involved parties, and society/the environment) by integrating social responsibility, ethics, and empathy into decision-making processes. 

The model moves beyond pure economic rationality and competition to a more holistic approach that incorporates social motivation and collective welfare: 

•                      Tripartite Focus: It transforms two-party negotiations into a three-dimensional process, ensuring outcomes benefit "me," "you," and "the community".

•                      Empathy and Social Justice: The framework suggests that cooperation is driven by empathy and social trust, not just competition. It emphasizes the "sensitization process" where participants consider community norms and social justice.

•                      Beyond Pareto Efficiency: While traditional models (like Pareto optimality) focus on resource allocation where no one can be made better off without making another worse off, the Win-Win-Win model introduces the community (the "C" factor) to add quality elements like equality and justice, aiming for a higher "equi-harmony" point that maximizes the triple utility for all involved.

•                      Behavioral Methodologies: The model uses behavioral analysis to transform perceptions and encourage "active participation" and self-organization within communities, particularly in local development and governance decisions. 

Application of AI in the Model

Recent research has explored the integration of Artificial Intelligence (AI) with the Papakonstantinidis model to enhance community growth and social cohesion. 

•                      Data Integration and Participatory Tools: AI platforms can process large-scale community data to optimize resource allocation, enhance participatory governance, and mediate stakeholder involvement, thus addressing systemic inequalities.

•                      Empowering Social Economy Enterprises (SEEs): AI marketing platforms can help SEEs (which prioritize social and environmental sustainability over profit) engage with stakeholders more effectively, bridging the gap with established businesses and promoting local sustainable growth.

Predicting Behavior: Unlike the standard stakeholder model, the Win-Win-Win model can use AI-compatible quantitative foundations to predict the behavior of bargainers by modeling individual decision-making, which helps in designing better public health strategies or local policies
DOI: 10.15468/dl.bvqg6g
title: Occurrence Download

description: A dataset containing 77556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ranunculus acris, Ranunculus acris, Ranunculus acris, Ranunculus acris)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 77556 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008859-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26188/30370351
title: Supporting data for "Moments-based quantum computation of the electric dipole moment of molecular systems"

description: Supporting data for the article "Moments-based quantum computation of the electric dipole moment of molecular systems".
DOI: 10.26188/30370351.v3
title: Supporting data for "Moments-based quantum computation of the electric dipole moment of molecular systems"

description: Supporting data for the article "Moments-based quantum computation of the electric dipole moment of molecular systems".
DOI: 10.26188/30370351.v2
title: Supporting data for "Moments-based quantum computation of the electric dipole moment of molecular systems"

description: Supporting data for the article "Moments-based quantum computation of the electric dipole moment of molecular systems".