cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.27691536
title: Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients

description: <b>Aim:</b> Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms. <b>Materials & methods:</b> A total of 178 patients with CIN (+) and 1511 with CIN (-) were included. <b>Results:</b> CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients. <b>Conclusion:</b> IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms. Contrast-induced nephropathy (CIN) is a significant complication that can arise from the use of contrast media during diagnostic or invasive procedures. Inflammatory markers such as the neutrophil-to-lymphocyte ratio and the C-reactive protein-to-albumin ratio were previously identified as good predictors of CIN, leading to the idea that the combination of these markers might be more useful for predicting CIN development than each parameter individually. A novel inflammatory marker (inflammatory prognostic index), which is the product of neutrophil/lymphocyte ratio (NLR) and C-reactive protein/albumin ratio (CAR) was an independent predictor of CIN in non-ST segment elevation myocardial infarction patients (NSTEMI). A nomogram including inflammatory-prognostic index (IPI) had high predictive and calibration abilities for estimating the risk of CIN development. IPI had higher discriminative ability than both NLR and CAR for discrimination of CIN (+) patients from CIN (-) ones. Naive-Bayes and k-nearest neighbors were the best algorithms for prediction of CIN development in NSTEMI. As a prediction tool machine learning (ML) algorithms including IPI, especially k-nearest neighbors might have net clinical benefit for detecting CIN development risk. In recent years, ML algorithms have been shown to better predict worse cardiovascular outcomes by analyzing the available healthcare data. The present study revealed that the ML algorithms might accurately predict the CIN developments when combined with well-known inflammatory marker in NSTEMI patients. A nomogram including IPI might be useful as a prediction tool for CIN development after coronary interventions in NSTEMI patients.
DOI: 10.6084/m9.figshare.27691536.v1
title: Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients

description: <b>Aim:</b> Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial infarction patients using a nomogram and performing machine learning (ML) algorithms. <b>Materials & methods:</b> A total of 178 patients with CIN (+) and 1511 with CIN (-) were included. <b>Results:</b> CIN (+) patients had higher IPI levels, and IPI was independently associated with CIN. A risk prediction nomogram including IPI had a higher predictive ability and good calibration. Naive Bayes and k-nearest neighbors were the best ML algorithms for the prediction of CIN patients. <b>Conclusion:</b> IPI might be used as an easily obtainable marker for CIN prediction using ML algorithms. Contrast-induced nephropathy (CIN) is a significant complication that can arise from the use of contrast media during diagnostic or invasive procedures. Inflammatory markers such as the neutrophil-to-lymphocyte ratio and the C-reactive protein-to-albumin ratio were previously identified as good predictors of CIN, leading to the idea that the combination of these markers might be more useful for predicting CIN development than each parameter individually. A novel inflammatory marker (inflammatory prognostic index), which is the product of neutrophil/lymphocyte ratio (NLR) and C-reactive protein/albumin ratio (CAR) was an independent predictor of CIN in non-ST segment elevation myocardial infarction patients (NSTEMI). A nomogram including inflammatory-prognostic index (IPI) had high predictive and calibration abilities for estimating the risk of CIN development. IPI had higher discriminative ability than both NLR and CAR for discrimination of CIN (+) patients from CIN (-) ones. Naive-Bayes and k-nearest neighbors were the best algorithms for prediction of CIN development in NSTEMI. As a prediction tool machine learning (ML) algorithms including IPI, especially k-nearest neighbors might have net clinical benefit for detecting CIN development risk. In recent years, ML algorithms have been shown to better predict worse cardiovascular outcomes by analyzing the available healthcare data. The present study revealed that the ML algorithms might accurately predict the CIN developments when combined with well-known inflammatory marker in NSTEMI patients. A nomogram including IPI might be useful as a prediction tool for CIN development after coronary interventions in NSTEMI patients.
DOI: 10.15468/dl.dghetj
title: Occurrence Download

description: A dataset containing 22832 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Globicephala melas (Traill, 1809)"
 ]
}

 The dataset includes 22832 records from 240 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028798-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mp5egg
title: Occurrence Download

description: A dataset containing 3198 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Iberis sempervirens L., Iberis sempervirens var. rosea Boiss., Iberis commutata Schott & Kotschy ex Boiss., Biauricularia sempervirens (L.) Bubani, 1901, Iberis serrulata Vis., Iberis sempervirens var. micropetala Doum., Iberis garrexiana All., Iberis arbuscula Spach, Iberis commutata Schott & Kotschy, Iberis sempervirens var. macropetala Doum., Aethionema longistylum Post, Iberis garrexiana var. macropetala Doum., Iberis sempervirens subsp. sempervirens, Biauricula sempervirens (L.) Bubani, Iberis sempervirens var. garrexiana (All.) Ces., Pass. & Gibelli, Iberis sempervirens subsp. garrexiana (All.) Nyman)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 3198 records from 152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028800-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bqmnu6
title: Occurrence Download

description: A dataset containing 14023 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Quercus garryana Douglas ex Hook."
 ]
}

 The dataset includes 14023 records from 160 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028801-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14362662
title: Dataset of Analisis Peran Auditor Teknologi Informasi dan Efektivitas Pengendalian Internal Terhadap Pemeriksaan Laporan Keuangan Klien Di KAP Jakarta

description: Dataset of Analisis Peran Auditor Teknologi Informasi dan Efektivitas Pengendalian Internal Terhadap Pemeriksaan Laporan Keuangan Klien Di KAP Jakarta
DOI: 10.5281/zenodo.14362663
title: Dataset of Analisis Peran Auditor Teknologi Informasi dan Efektivitas Pengendalian Internal Terhadap Pemeriksaan Laporan Keuangan Klien Di KAP Jakarta

description: Dataset of Analisis Peran Auditor Teknologi Informasi dan Efektivitas Pengendalian Internal Terhadap Pemeriksaan Laporan Keuangan Klien Di KAP Jakarta
DOI: 10.5281/zenodo.14283655
title: High-resolution UAV Orthomosaic and DSM Dataset - Blueberry Hill (Aklavik, NWT, CA) 5 cm GSD [2022]

description: This dataset contains a high-resolution (5 cm) orthomosaic and Digital Surface Model (DSM) generated from aerial imagery captured by a DJI Mini 2 drone. The data was acquired on September 24, 2022, and covers an area of 1.77 km². The data was acquiered as part of the UndercoverEisAgenten project funded by German Federal Ministry of Education and Research together with students of Moose Kerr School, Aklavik, NWT, Canada. The processing parameters used in Metashape are detailed in the attached report.
DOI: 10.5281/zenodo.14283656
title: High-resolution UAV Orthomosaic and DSM Dataset - Blueberry Hill (Aklavik, NWT, CA) 5 cm GSD [2022]

description: This dataset contains a high-resolution (5 cm) orthomosaic and Digital Surface Model (DSM) generated from aerial imagery captured by a DJI Mini 2 drone. The data was acquired on September 24, 2022, and covers an area of 1.77 km². The data was acquiered as part of the UndercoverEisAgenten project funded by German Federal Ministry of Education and Research together with students of Moose Kerr School, Aklavik, NWT, Canada. The processing parameters used in Metashape are detailed in the attached report.
DOI: 10.15468/dl.6673xd
title: Occurrence Download

description: A dataset containing 21453 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Microstegium vimineum (Trin.) A.Camus"
 ]
}

 The dataset includes 21453 records from 255 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028802-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mufw3b
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Inula helenium subsp. helenium, Helenium grandiflorum Gilib., Aster officinalis All., Inula anthelmintica Salisb., Inula crocea Krock, Helenium officinale Kuntze, Inula grandiflora Gray, Inula helenia St.-Lag., Inula campana Garsault, Aster helenium (L.) Scop., Corvisartia helenium (L.) Mérat)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 11 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028804-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/p48qt7
title: Coleção de Copepoda da Universidade Federal do Amapá

description: O acervo do subfilo Crustacea é composto por Copepoda que tem 2 espesimes. Todos os exemplares foram coletados no estado do Amapá.
DOI: 10.15468/dl.9m3t6a
title: Occurrence Download

description: A dataset containing 23090 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Persicaria longiseta (Bruijn) Kitag."
 ]
}

 The dataset includes 23090 records from 297 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028806-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mdga7a
title: Occurrence Download

description: A dataset containing 352 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Carex stenostachys var. cuneata (Ohwi) Ohwi & T.Koyama",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 352 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006058-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1110738.10fu
title: SH1110738.10FU

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/sh1110739.10fu
title: SH1110739.10FU

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: 10.3886/e243008
title: Peter University, Achina-Onneh Anambra State 2026/27 JUPEB/IJMB Form is out ☎️(08039478772) DR MERCY☎️ (+23408039478772) Direct-Entry Form, Transfer Form, Change of institution form, Change of Course form, Post graduate Diploma form

description: Peter University, Achina-Onneh Anambra State 2026/27 JUPEB/IJMB Form is out ☎️(08039478772) DR MERCY☎️ (+23408039478772) Direct-Entry Form, Transfer Form, Change of institution form, Change of Course form, Post graduate Diploma form, Masters form, Phd form call ☎️(+23408039478772) for more inquiry and admission assistance.
DOI: 10.15156/bio/sh1110740.10fu
title: SH1110740.10FU

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/sh1110744.10fu
title: SH1110744.10FU

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/sh1110745.10fu
title: SH1110745.10FU

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/sh1110748.10fu
title: SH1110748.10FU

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/sh1110750.10fu
title: SH1110750.10FU

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/sh1110753.10fu
title: SH1110753.10FU

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/sh1110755.10fu
title: SH1110755.10FU

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/sh1110757.10fu
title: SH1110757.10FU

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.