cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24948683
title: A review on recent driver safety systems and its emerging solutions

description: Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. <b>Abbreviations</b> IoT: Internet of things; CNN: Convolutional Neural Network; SVM: Support vector machine; HRV: Heart rate variability; RRI: R-R Interval; MSPC: Multivariate Statistical process control; EAR: Eye aspect ratio; HUD: Head-up display; GPS: Global positioning system; CAN: Controller area network; GPU: Graphics processing unit; IR: Infrared; GSM: Global system for mobile communication; EEG: Electroencephalogram; PCA: Principal component analysis; SVC: Support vector classifier; SdsAEs: Stacked denoising sparse autoencoders; ECG: Electrocardiogram; LED: Light emitting diode; NFC: Near field communication; PSO: Personal security officer; PPG: Photoplethysmography; EDA: Electrodermal activity; EMG: Electromyography; LCD: Liquid crystal display; RF SoCs: Radiofrequency system on chip; PLR: Piecewise linear representation; BAC: Blood alcohol content; BPNN: Backpropagation Neural Network; ADSD: Automated driver sleepiness detection; EOG: Electroocoulogram; KNN: K nearest neighbor; CBR: Case-based reasoning; RF: Random forest; NIR: Near-infrared; LBP: Local binary pattern; PERCLOS: Percentage of Eye Closure; SVD: Singular value decomposition; FFT: Fast Fourier transform; LSTM: Long short-term memory; DDD: Drunk driver detection; BLE: Bluetooth low energy; SWM: Steering wheel movements; M-SVM: Mobile-based Support Vector Machine; AI: Artificial intelligence; ML: Machine learning; DL: Deep learning; PCA: Principal component analysis; IPCA: Incremental principal component analysis; ANN: Artificial neural network; CAV: Connected and automated vehicles
DOI: 10.6084/m9.figshare.24948683.v1
title: A review on recent driver safety systems and its emerging solutions

description: Road safety and accident prevention are critical concerns in modern transportation. This paper presents a comprehensive survey of driver safety systems, focusing on the latest advancements in this field. We analyze the existing literature to identify key research trends in driver safety systems, encompassing various categories of solutions. Our survey delves into the reasons behind road accidents and assesses the effectiveness of emerging technologies and solutions in accident prevention. By categorizing and evaluating these solutions based on the Internet of Things and Machine Learning, we provide valuable insights into the landscape of road accident detection and prevention systems. This survey not only highlights the current state of the art but also serves as a reference for future research and innovation in the domain of driver safety. <b>Abbreviations</b> IoT: Internet of things; CNN: Convolutional Neural Network; SVM: Support vector machine; HRV: Heart rate variability; RRI: R-R Interval; MSPC: Multivariate Statistical process control; EAR: Eye aspect ratio; HUD: Head-up display; GPS: Global positioning system; CAN: Controller area network; GPU: Graphics processing unit; IR: Infrared; GSM: Global system for mobile communication; EEG: Electroencephalogram; PCA: Principal component analysis; SVC: Support vector classifier; SdsAEs: Stacked denoising sparse autoencoders; ECG: Electrocardiogram; LED: Light emitting diode; NFC: Near field communication; PSO: Personal security officer; PPG: Photoplethysmography; EDA: Electrodermal activity; EMG: Electromyography; LCD: Liquid crystal display; RF SoCs: Radiofrequency system on chip; PLR: Piecewise linear representation; BAC: Blood alcohol content; BPNN: Backpropagation Neural Network; ADSD: Automated driver sleepiness detection; EOG: Electroocoulogram; KNN: K nearest neighbor; CBR: Case-based reasoning; RF: Random forest; NIR: Near-infrared; LBP: Local binary pattern; PERCLOS: Percentage of Eye Closure; SVD: Singular value decomposition; FFT: Fast Fourier transform; LSTM: Long short-term memory; DDD: Drunk driver detection; BLE: Bluetooth low energy; SWM: Steering wheel movements; M-SVM: Mobile-based Support Vector Machine; AI: Artificial intelligence; ML: Machine learning; DL: Deep learning; PCA: Principal component analysis; IPCA: Incremental principal component analysis; ANN: Artificial neural network; CAV: Connected and automated vehicles
DOI: 10.6084/m9.figshare.25218839
title: <b>Tabla 1 y 2. Matriz de Relevamiento Instituciones Formales, informales y actores en el marco de la GPR</b>

description: Este trabajo caracteriza el marco institucional del ciclo presupuestario de la Universidad Nacional del Córdoba en cuanto a instituciones formales, informales y actores utilizando el modelo de gestión para resultados y sus instrumentos. La motivación que impulsa este trabajo se encuentra en la necesidad de entender cómo funciona el ciclo presupuestario de la UNC, cómo se asignan los recursos y si esa asignación responde a una instancia previa de planificación. Para responder estos interrogantes se realizó un relevamiento documental, entrevistas, y un proceso de observación participante. Todo ello sirvió para construir una matriz que permitió caracterizar el marco institucional y los instrumentos de gestión para resultados. Posteriormente, sobre esa caracterización se elaboró un estado de situación respecto de la gestión para resultados, así como unas recomendaciones para una posible implementación.
DOI: 10.6084/m9.figshare.25218839.v2
title: <b>Tabla 1 y 2. Matriz de Relevamiento Instituciones Formales, informales y actores en el marco de la GPR</b>

description: Este trabajo caracteriza el marco institucional del ciclo presupuestario de la Universidad Nacional del Córdoba en cuanto a instituciones formales, informales y actores utilizando el modelo de gestión para resultados y sus instrumentos. La motivación que impulsa este trabajo se encuentra en la necesidad de entender cómo funciona el ciclo presupuestario de la UNC, cómo se asignan los recursos y si esa asignación responde a una instancia previa de planificación. Para responder estos interrogantes se realizó un relevamiento documental, entrevistas, y un proceso de observación participante. Todo ello sirvió para construir una matriz que permitió caracterizar el marco institucional y los instrumentos de gestión para resultados. Posteriormente, sobre esa caracterización se elaboró un estado de situación respecto de la gestión para resultados, así como unas recomendaciones para una posible implementación.
DOI: 10.6084/m9.figshare.25218839.v1
title: <b>Tabla 1 y 2. Matriz de Relevamiento Instituciones Formales, informales y actores en el marco de la GPR</b>

description: Este trabajo caracteriza el marco institucional del ciclo presupuestario de la Universidad Nacional del Córdoba en cuanto a instituciones formales, informales y actores utilizando el modelo de gestión para resultados y sus instrumentos. La motivación que impulsa este trabajo se encuentra en la necesidad de entender cómo funciona el ciclo presupuestario de la UNC, cómo se asignan los recursos y si esa asignación responde a una instancia previa de planificación. Para responder estos interrogantes se realizó un relevamiento documental, entrevistas, y un proceso de observación participante. Todo ello sirvió para construir una matriz que permitió caracterizar el marco institucional y los instrumentos de gestión para resultados. Posteriormente, sobre esa caracterización se elaboró un estado de situación respecto de la gestión para resultados, así como unas recomendaciones para una posible implementación.
DOI: 10.6084/m9.figshare.20156782.v1
title: Additional file 2 of Ectopic expression of a combination of 5 genes detects high risk forms of T-cell acute lymphoblastic leukemia

description: Additional file 2.
DOI: 10.15468/dl.mqgdwj
title: Occurrence Download

description: A dataset containing 908 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "TaxonKey is Avena fatua L."
 ]
}

 The dataset includes 908 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081858-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jbrs3u
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria kaoensis A.C.Sm."
 ]
}

 The dataset includes 25 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081859-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2xempw
title: Occurrence Download

description: A dataset containing 517 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudognaphalium oligandrum (DC.) Hilliard & B.L.Burtt"
 ]
}

 The dataset includes 517 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081860-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r2axjh
title: Occurrence Download

description: A dataset containing 759 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pternandra coerulescens Jack"
 ]
}

 The dataset includes 759 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081861-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dy8emk
title: Occurrence Download

description: A dataset containing 111876 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acer rubrum L."
 ]
}

 The dataset includes 111876 records from 379 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081790-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.em5ege
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria karemaensis Sohmer"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081862-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.veunb9
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudognaphalium petitianum (A.Rich.) Mesfin"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081863-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.et4vsr
title: Occurrence Download

description: A dataset containing 84 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Uncaria perrottetii (A.Rich.) Merr."
 ]
}

 The dataset includes 84 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081864-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kx3962
title: Occurrence Download

description: A dataset containing 157 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pternandra cogniauxii M.P.Nayar"
 ]
}

 The dataset includes 157 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081865-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p5jc97
title: Occurrence Download

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

 The dataset includes 13 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081866-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wxr6tu
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudognaphalium richardianum (Cufod.) Hilliard & B.L.Burtt"
 ]
}

 The dataset includes 31 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081867-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25523737
title: Clinical Characteristics and Risk Factors for In-Hospital Mortality in Surgical Patients with Abdominal Sepsis: A Retrospective Multicenter Study

description: The dataset uploaded here is the raw data for this study, and these data can be further analyzed.The clinical dataset for this study was derived from two prominent healthcare institutions: the Chinese People's Liberation Army (PLA) General Hospital, which boasts 4,000 patient beds, and the PLA Air Force Medical Center, equipped with 1,500 patient beds. We undertook a retrospective analysis of the medical records of patients with abdominal sepsis who underwent surgical interventions in the operating room under general anesthesia between January 2008 and December 2022. Data collection was executed in a standardized fashion, adhering to stringent definitions to ascertain data integrity. The need for patient consent is waived due to the retrospective study design.
DOI: 10.15468/dl.papx5f
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pternandra coriacea (Cogn.) M.P.Nayar"
 ]
}

 The dataset includes 13 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081868-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.62c9jb
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria kelelensis Valeton"
 ]
}

 The dataset includes 14 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081869-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.94736s
title: Occurrence Download

description: A dataset containing 489 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudognaphalium sandwicensium (Gaudich.) Anderb."
 ]
}

 The dataset includes 489 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081870-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w6psz8
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pternandra crassicalyx J.F.Maxwell"
 ]
}

 The dataset includes 78 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081871-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.grd5h7
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria kentii Razafim. & B.Bremer"
 ]
}

 The dataset includes 7 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081872-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8wpxme
title: Occurrence Download

description: A dataset containing 1537 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pseudognaphalium undulatum (L.) Hilliard & B.L.Burtt"
 ]
}

 The dataset includes 1537 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081873-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gygxtv
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pternandra echinata Jack"
 ]
}

 The dataset includes 279 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081874-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.56xtaz
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Psychotria keyensis Warb."
 ]
}

 The dataset includes 3 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081875-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh3434221.08fu
title: SH3434221.08FU

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/sh3434222.08fu
title: SH3434222.08FU

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/sh3434223.08fu
title: SH3434223.08FU

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/sh3434224.08fu
title: SH3434224.08FU

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.