cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0174295.10fu
title: SH0174295.10FU

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.18199456
title: semantic-scholar-coauth-sample

description: Constructed from a subsample of the Semantic Scholar Open Research Corpus, this dataset is a simplicial complex where nodes are authors and simplices are sets of co-authors on papers.Values on every simplex indicate the sum of citations of all papers co-authored at least by the corresponding set of authors.

Find the dataset details in AHORN.
DOI: 10.15156/bio/sh0174296.10fu
title: SH0174296.10FU

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/sh0174297.10fu
title: SH0174297.10FU

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/sh0174298.10fu
title: SH0174298.10FU

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/xazabc
title: Revealing anole diversity in the highlands of the Northern Andes: New and resurrected species of the Anolis heterodermus species group

description: This dataset contains the digitized treatments in Plazi based on the original journal article Moreno-Arias, Rafael A., Mendez-Galeano, Miguel A., Beltran, Ivan, Vargas-Ramirez, Mario (2023): Revealing anole diversity in the highlands of the Northern Andes: New and resurrected species of the Anolis heterodermus species group. Vertebrate Zoology 73: 161-188, DOI: http://dx.doi.org/10.3897/vz.73.e94265, URL: http://dx.doi.org/10.3897/vz.73.e94265
DOI: 10.15468/dl.hzntzb
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ovulariopsis broussonetiae-papyriferae Sawada"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009293-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tww44v
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Festuca arundinacea var. arundinacea x Lolium perenne"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009294-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8dsjqq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phacelia tanacetifolia Thurb."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009295-250127130748423/datasets/export for details.
DOI: 10.15468/dl.7v9ube
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lolium perenne x Schedonorus arundinaceus subsp. arundinaceus"
 ]
}

 The dataset includes 21 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009297-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.55krja
title: Occurrence Download

description: A dataset containing 57817 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pelophylax perezi (López-Seoane, 1885)"
 ]
}

 The dataset includes 57817 records from 193 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009296-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kncvqm
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phacelia tanacetifolia DC."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009298-250127130748423/datasets/export for details.
DOI: 10.15468/dl.46dcj2
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lolium perenne x Schedonorus arundinaceus ssp. uechtritzianus"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009299-250127130748423/datasets/export for details.
DOI: 10.15468/dl.85z3uv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phacelia tanacetifolia Thurb. ex Torr."
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009301-250127130748423/datasets/export for details.
DOI: 10.15468/dl.qsjwss
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Festuca arundinacea var. aspera x Lolium perenne"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009302-250127130748423/datasets/export for details.
DOI: 10.15468/dl.2jpbsp
title: Occurrence Download

description: A dataset containing 59288 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bidens pilosa L."
 ]
}

 The dataset includes 59288 records from 967 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009300-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.469k7y
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lolium perenne x Schedonorus arundinaceus subsp. uechtritzianus"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009304-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vxrgsp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lolium perenne x Schedonorus arundinaceus ssp. arundinaceus"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009307-250127130748423/datasets/export for details.
DOI: 10.15468/dl.j46yfs
title: Occurrence Download

description: A dataset containing 59047 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sorghum halepense (L.) Pers."
 ]
}

 The dataset includes 59047 records from 1068 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009306-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p6x9uv
title: Occurrence Download

description: A dataset containing 736 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry MULTIPOLYGON(((-90.54843 7.19737,-77.65186 7.19737,-77.65186 14.32581,-90.54843 14.32581,-90.54843 7.19737)))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Leopardus pardalis",
 "Year 1990-2025"
 ]
}

 The dataset includes 736 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040985-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57912/30167998
title: Sleep and affect in individuals with borderline personality disorder features: A daily diary study - Dataset

description: Given that people with Borderline Personality Disorder (BPD) features are vulnerable to emotion dysregulation, they might be particularly sensitive to the emotional impacts of poor sleep quality and low sleep quantity. In the present study, we evaluated whether BPD features moderate the relationship between daily sleep, measured objectively (total sleep time) and subjectively (perceived sleep quality), and self-reports of positive and negative affect. A sample of 83 cohabitating couples (<i>n</i>=166) were recruited for a three-week daily diary study. Participants completed a BPD symptom measure at baseline, perceived sleep quality assessment each morning, and daily emotion assessment each evening. Total sleep time was assessed daily using a FitBit activity monitor. Mixed-model regression analyses revealed a significant interaction between subjective sleep quality and overall BPD features in predicting daily levels of negative affect, even after statistical control of depression. Participants with higher BPD features exhibited greater negative affect on days when they reported worse sleep quality, as compared to participants with lower BPD features. Secondary analyses showed that this interaction seemed to be particularly impactful for anger, but not anxiety or sadness. In contrast, there was no significant interaction effect between BPD features and objectively assessed total sleep time in predicting daily emotion. These results suggest that the emotional impact of poor subjective sleep quality might be more pronounced for those with BPD features.
DOI: 10.15468/dl.2byv3j
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erodium stephanianum",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 23 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012220-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.z9crf2
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Atelopus exiguus"
 ]
}

 The dataset includes 84 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012223-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tev7me
title: Occurrence Download

description: A dataset containing 17196 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lyssavirus"
 ]
}

 The dataset includes 17196 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012222-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17605/osf.io/yxrke
title: Multimethod data on mental workload in pilots

description: Neurophysiological (EEG, fNIRS), eye-tracking, performance, subjective and demographic data for the publication "Examining physiological parameters of mental workload in the cockpit: A multimethod approach"
DOI: 10.6084/m9.figshare.29984347
title: Mapping products of shantytowns in Chinese cities

description: Shantytowns are characterized by harsh living conditions and dilapidated housing, and effective extraction and analysis methods are required in urban planning. This product extracts the scope of shantytowns from high-resolution remote sensing images by using the Multi-scale Global-Local Information Interactive Attention Transformer (MGLI-Former) and the Scene Classification-Assisted Texture Feature Enhanced Multi-scale Network (STMNet).
DOI: 10.6084/m9.figshare.29984347.v2
title: Mapping products of shantytowns in Chinese cities

description: 贫民窟地区以居住条件恶劣、住房破旧为特征，在城市规划中需要有效的提取和分析方法。该产品利用多尺度全局-局部信息交互注意力转换器模型（MGLI-Former）和场景分类辅助纹理特征增强多尺度网络（STMNet）从高分辨率遥感影像中提取贫民窟信息。
DOI: 10.6084/m9.figshare.29984347.v3
title: Mapping products of shantytowns in Chinese cities

description: Shantytowns are characterized by harsh living conditions and dilapidated housing, and effective extraction and analysis methods are required in urban planning. This product extracts the scope of shantytowns from high-resolution remote sensing images by using the Multi-scale Global-Local Information Interactive Attention Transformer (MGLI-Former) and the Scene Classification-Assisted Texture Feature Enhanced Multi-scale Network (STMNet).
DOI: 10.6084/m9.figshare.29627669
title: Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes

description: Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this article, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic of scoring rules used in forecasting literature. In this context, we propose and study a risk function adapted to extremal classifiers. The inferential properties of our empirical risk estimator are derived under the framework of multivariate regular variation and hidden regular variation. A simulation study compares different classifiers and indicates their performance with respect to our risk function. To conclude, we apply our framework to the analysis of extreme river discharges in the Danube river basin. The application compares different predictive algorithms and test their capacity at forecasting river discharges from other river stations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29627669.v2
title: Evaluation of Binary Classifiers for Asymptotically Dependent and Independent Extremes

description: Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this article, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic of scoring rules used in forecasting literature. In this context, we propose and study a risk function adapted to extremal classifiers. The inferential properties of our empirical risk estimator are derived under the framework of multivariate regular variation and hidden regular variation. A simulation study compares different classifiers and indicates their performance with respect to our risk function. To conclude, we apply our framework to the analysis of extreme river discharges in the Danube river basin. The application compares different predictive algorithms and test their capacity at forecasting river discharges from other river stations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29627669.v1
title: Evaluation of binary classifiers for asymptotically dependent and independent extremes

description: Machine learning classification methods usually assume that all possible classes are sufficiently present within the training set. Due to their inherent rarities, extreme events are always under-represented and classifiers tailored for predicting extremes need to be carefully designed to handle this under-representation. In this paper, we address the question of how to assess and compare classifiers with respect to their capacity to capture extreme occurrences. This is also related to the topic of scoring rules used in forecasting literature. In this context, we propose and study a risk function adapted to extremal classifiers. The inferential properties of our empirical risk estimator are derived under the framework of multivariate regular variation and hidden regular variation. A simulation study compares different classifiers and indicates their performance with respect to our risk function. To conclude, we apply our framework to the analysis of extreme river discharges in the Danube river basin. The application compares different predictive algorithms and test their capacity at forecasting river discharges from other river stations.
DOI: 10.5281/zenodo.17188152
title: Regional attention transformer for Open-Quantum-Dynamics Dataset (v1.0)

description: # EIT Open-Quantum-Dynamics Dataset (v1.0)


This dataset contains synthetic datasets and evlauation results in:

“Learning spatially structured open quantum dynamics with regional-attention transformers”.


## Contents

``` 

- quantum_memory/: datasets and eval results in the EIT quantum memory learning.

- eval/

- evaluation_smooth_learning_2_150epoches/

evaluation of Quformer 4.4M in decoherence free datasets(ID&OOD)

- evaluation_smooth_learning_2_150epoches_deco/

evaluation of Quformer 4.4M in decoherence datasets(ID&OOD)

- evaluation_smooth_learning_2_150epoches_6layers/

evaluation of Quformer Var1 6.3M in decoherence free datasets(ID&OOD)

- evaluation_smooth_learning_2_150epoches_6layers_deco/

evaluation of Quformer Var1 6.3M in decoherence datasets(ID&OOD)

- evaluation_var1_150epoches/

evaluation of Quformer Var2 6.1M in decoherence free datasets(ID&OOD)

- evaluation_var1_150epoches_deco/

evaluation of Quformer Var2 6.1M in decoherence datasets(ID&OOD)

 

- syntheticQuantumMemoryData/

- data/

- decoherence_data/

datasets with decoherence. Naming: *_in_distribution: used for train/eval in model training; *_id_extra, *_OOD*: used for evaluation ID and OOD sets.

- decoherenceFree_data/

decoherence free datasets. Naming: in_distribution: used for train/eval in model training; in_distribution_extra, OOD*: used for evaluation ID and OOD sets.


- single_qubit/: datasets and eval results in the single qubit learning.

- data/

synthetic datasets.

- evaluation/

evlaution of ID(*_0sigma) and OODs(*_1sigma to *_3sigma)

```


## Use with Github code

Download corresponding code from https://github.com/Dounan662/RegionalAttentionOpenQuantum


## License

Data: CC BY 4.0. Please cite the DOI below.


## Citation

Du, D. et al., Regional attention transformer for Open-Quantum-Dynamics Dataset, Zenodo, v1.0, DOI: <to be filled after publish>.

 

Additional support was provided by the Stony Brook Foundation through the Quantum Network Research Center (SBF 248090)