cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.5bra6e
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balclutha incisa (Matsumura, 1902)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0225182-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tn2a5b
title: Occurrence Download

description: A dataset containing 105 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gurania capitata (Poepp. & Endl.) Cogn."
 ]
}

 The dataset includes 105 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0233995-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cyef4t
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Deporaus betulae Stephens, 1831",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0225295-220831081235567/datasets/export for details.
DOI: 10.15468/dl.mqx5xh
title: Occurrence Download

description: A dataset containing 1106 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gurania acuminata Cogn."
 ]
}

 The dataset includes 1106 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0233983-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g36bpm
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eucurtiopsis ohtanii (K.Sawada, 1994)",
 "HasCoordinate is true",
 "Country is Japan",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0225276-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w62v28
title: Occurrence Download

description: A dataset containing 4568882 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 1970-2023"
 ]
}

 The dataset includes 4568882 records from 2260 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0265931-230224095556074/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18047037
title: Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland

description: Der Datensatz "Intensivkapazitäten und COVID-19-Intensivbettenbelegung in Deutschland" des Robert Koch-Instituts dokumentiert die tägliche intensivmedizinische Versorgungslage seit der COVID-19-Pandemie. Basierend auf Meldungen aller intensivbettenführenden Krankenhäuser in Deutschland erfasst das DIVI-Intensivregister Echtzeitdaten zu belegten und freien Intensivbetten. Die Erhebung differenziert nach Altersgruppen, Regionen und Versorgungsstufen. COVID-19-Fälle auf Intensivstationen werden gesondert ausgewiesen. Die Daten stehen aggregiert auf Bundes-, Landes- und Kreisebene zur Verfügung. Damit bildet der Datensatz eine Grundlage für die Überwachung von Kapazitäten, die Koordination von Behandlungskapazitäten und politische Entscheidungsprozesse während der Pandemie und darüber hinaus.
DOI: 10.15468/dl.xp7g4e
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Phrynopus juninensis"
 ]
}

 The dataset includes 21 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0064737-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/888d3r
title: Nomenclature and systematics of two Cocconeis species (Bacillariophyta) from Lake Baikal: Cocconeis baicalensis and Cocconeis skvortzowii

description: This dataset contains the digitized treatments in Plazi based on the original journal article Gogorev, Rinat, Yurchak, Maria, Sokolova, Irina, Glushchenko, Anton, Kulikovskiy, Maxim (2024): Nomenclature and systematics of two Cocconeis species (Bacillariophyta) from Lake Baikal: Cocconeis baicalensis and Cocconeis skvortzowii. PhytoKeys 247: 219-248, DOI: 10.3897/phytokeys.247.131353AbstractThe study provides nomenclatural history, morphological characteristics and taxonomy of Cocconeis baicalensis and C. skvortzowii described by B. V. Skvortzov from Lake Baikal. The lecto- and epitypification of the names are made, based on the K. I. Meyer and A. P. Skabichevsky materials, ensuring compliance with current nomenclatural standards. The morphological traits of these species were thoroughly examined using light microscopy (LM) nd scanning electron microscopy (SEM), leading to refined diagnoses and the clarification of previously confused synonymy.
DOI: 10.15468/vy7rtj
title: The History and introduction of the Daurian Lily Lilium pensylvanicum and the new combination L. pensylvanicum var. alpinum (Liliaceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Compton, James A., Sytin, Andrej K. (2023): The History and introduction of the Daurian Lily Lilium pensylvanicum and the new combination L. pensylvanicum var. alpinum (Liliaceae). PhytoKeys 236: 215-247, DOI: http://dx.doi.org/10.3897/phytokeys.236.111741, URL: http://dx.doi.org/10.3897/phytokeys.236.111741
DOI: 10.15468/dl.g5k74q
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hintonia octomera (Hemsl.) Bullock)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017758-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14823965
title: Deep transfer learning for detection of breast arterial calcifications on mammograms a comparative study

description: Abstract



Introduction: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.

Material and methods: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.

Results: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.

Conclusion: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.

Relevance statement: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.

Key points: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.
DOI: 10.5281/zenodo.14823966
title: Deep transfer learning for detection of breast arterial calcifications on mammograms a comparative study

description: Abstract



Introduction: Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs.

Material and methods: Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations.

Results: The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images.

Conclusion: Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources.

Relevance statement: Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs.

Key points: • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16's superior performance in localizing BAC.
DOI: 10.5281/zenodo.14823888
title: Appendix for the article "Socratic AI Against Disinformation: Improving Critical Thinking to Recognize Disinformation Using Socratic AI"

description: You will find two files related to the paper: Duelen, A., Jennes, I., & Van den Broeck, W. (2024). Socratic AI Against Disinformation: Improving Critical Thinking to Recognize Disinformation Using Socratic AI. In IMX '24: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 375-381. https://doi.org/10.1145/3639701.3663640

The 'Playbook for Living Labs partners' contains all steps of the methodological framework implemented for the execution of the Living Labs.

The excel file 'BE Results Living Labs questionnaire' contains the results from the questionnaire participants completed after testing the Socratic AI-chatbot.
DOI: 10.5281/zenodo.14823889
title: Appendix for the article "Socratic AI Against Disinformation: Improving Critical Thinking to Recognize Disinformation Using Socratic AI"

description: You will find two files related to the paper: Duelen, A., Jennes, I., & Van den Broeck, W. (2024). Socratic AI Against Disinformation: Improving Critical Thinking to Recognize Disinformation Using Socratic AI. In IMX '24: Proceedings of the 2024 ACM International Conference on Interactive Media Experiences, 375-381. https://doi.org/10.1145/3639701.3663640

The 'Playbook for Living Labs partners' contains all steps of the methodological framework implemented for the execution of the Living Labs.

The excel file 'BE Results Living Labs questionnaire' contains the results from the questionnaire participants completed after testing the Socratic AI-chatbot.
DOI: 10.15468/dl.v99s7a
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hintonia standleyana Bullock)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017759-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4121/2f4145c6-74cb-4b67-8f17-99d45e75fd43
title: Data underlying the publication: Realization of a one-dimensional topological insulator in ultrathin germanene nanoribbons

description: This is the dataset used in the publication: Realization of a one-dimensional topological insulator in ultrathin germanene nanoribbons. In this publication, we present a transition from a 2D topological insulator with 1D edge states towards a 1D topological insulator with 0D end states in ultrathin germanene nanoribbons. We use STM, STS, uLEED and theory to explain our findings. The dataset presented here contains STS and STM data obtained using an Omicron Low-Temperature Scanning Tunneling Microscope (LT-STM) at 77K.
DOI: 10.4121/2f4145c6-74cb-4b67-8f17-99d45e75fd43.v1
title: Data underlying the publication: Realization of a one-dimensional topological insulator in ultrathin germanene nanoribbons

description: This is the dataset used in the publication: Realization of a one-dimensional topological insulator in ultrathin germanene nanoribbons. In this publication, we present a transition from a 2D topological insulator with 1D edge states towards a 1D topological insulator with 0D end states in ultrathin germanene nanoribbons. We use STM, STS, uLEED and theory to explain our findings. The dataset presented here contains STS and STM data obtained using an Omicron Low-Temperature Scanning Tunneling Microscope (LT-STM) at 77K.
DOI: 10.15468/dl.u3wcsx
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hoffmannia cauliflora Hemsl.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017760-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9zdta
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Material sample",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Osedax japonicus Fujikura, Fujiwara & Kawato, 2006, Osedax frankpressi Rouse, Goffredi & Vrijenhoek, 2004, Osedax rubiplumus Rouse, Goffredi & Vrijenhoek, 2004, Osedax mucofloris Glover, Kallstrom, Smith & Dahlgren, 2005)"
 ]
}

 The dataset includes 191 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017761-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e5vuq5
title: Occurrence Download

description: A dataset containing 1732328 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Aegolius funereus (Linnaeus, 1758), Ninox scutulata (Raffles, 1822), Ketupa zeylonensis (J.F.Gmelin, 1788), Strix leptogrammica Temminck, 1832, Strix aluco Linnaeus, 1758, Otus scops (Linnaeus, 1758), Bubo bubo (Linnaeus, 1758), Phodilus badius (Horsfield, 1821), Cacomantis merulinus (Scopoli, 1786), Ducula aenea (Linnaeus, 1766), Ducula badia (Raffles, 1822), Phodilus assimilis Hume, 1877, Treron curvirostra (J.F.Gmelin, 1789))"
 ]
}

 The dataset includes 1732328 records from 3683 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017740-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh0182164.10fu
title: SH0182164.10FU

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/sh0182165.10fu
title: SH0182165.10FU

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/sh0182166.10fu
title: SH0182166.10FU

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/sh0182167.10fu
title: SH0182167.10FU

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/sh0182168.10fu
title: SH0182168.10FU

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.