cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18335527
title: The Usage Paradox in Digital Education and Its Solution: An Empirical Study Based on the Influence Mechanism of Cloud Class Teaching Model in China

description: This dataset originates from a questionnaire survey investigating the impact of a cloud-based teaching model on the academic performance of students at a county high school in Fengjie, Chongqing,China, and its underlying mechanisms. The study specifically focuses on the mediating role of digital resource utilization and the crucial moderating effect of student gender. It empirically examines the "use paradox" in digital education, revealing how social factors construct the effectiveness of technology-integrated instruction.
DOI: 10.5281/zenodo.15384325
title: Features mamma-mia
DOI: 10.5281/zenodo.15384326
title: Features mamma-mia
DOI: 10.5281/zenodo.18335526
title: The Usage Paradox in Digital Education and Its Solution: An Empirical Study Based on the Influence Mechanism of Cloud Class Teaching Model in China

description: This dataset originates from a questionnaire survey investigating the impact of a cloud-based teaching model on the academic performance of students at a county high school in Fengjie, Chongqing,China, and its underlying mechanisms. The study specifically focuses on the mediating role of digital resource utilization and the crucial moderating effect of student gender. It empirically examines the "use paradox" in digital education, revealing how social factors construct the effectiveness of technology-integrated instruction.
DOI: 10.6084/m9.figshare.31123482
title: Experimentally induced lesions of an emerging pathogenic strain of <i>Salmonella</i> Gallinarum in broiler chicks

description: An emerging highly pathogenic strain of <i>Salmonella</i> Gallinarum biovar Gallinarum (SG) was isolated from a severe natural outbreak of fowl typhoid, with enteric and systemic lesions, and used for the infection of broiler chicks. This strain was genetically characterized by a greater diversity of virulence and antimicrobial resistance genes. A total of 160 1-day-old Ross broiler chicks were divided into four groups and received oral and intranasal inoculations of SG at different cell concentrations. From 3-12 days post-inoculation, <i>postmortem</i> examinations were performed on the chicks to collect samples for bacteriological, histopathological, and immunohistochemical examinations. Typical gross and histological lesions such as fibrinonecrotic and/or histiocytic hepatitis, splenitis, and myocarditis were found in chicks from all groups. Unexpected lesions included marked transmural and non-transmural histiocytic enteritis, laryngitis and tracheitis, interstitial pneumonia and glomerulonephritis, glomerular thrombosis, fibrinous and histiocytic bone marrow myelitis, necrotic and histiocytic bursitis, and necrotic and histiocytic encephalitis. Within lesions, SG was detected by immunohistochemistry in all affected tissues, as well as by retrieval of the pathogen from liver and spleen in bacteriology. Intranasally challenged chicks had more pathogenic presentation of the disease changes, compared to orally challenged chicks, with a greater number of affected organs and intestinal segments. These results highlight the importance of the respiratory route for the pathogenesis of the infection and disease. An emerging highly virulent <i>S.</i> Gallinarum strain caused uncommon respiratory and intestinal lesions.The intranasal route of challenge caused a more severe and systemic disease, with broader organ involvement.The emerging strain targeted novel organs including larynx, trachea, bursa of Fabricius, and bone marrow. An emerging highly virulent <i>S.</i> Gallinarum strain caused uncommon respiratory and intestinal lesions. The intranasal route of challenge caused a more severe and systemic disease, with broader organ involvement. The emerging strain targeted novel organs including larynx, trachea, bursa of Fabricius, and bone marrow.
DOI: 10.6084/m9.figshare.31123482.v1
title: Experimentally induced lesions of an emerging pathogenic strain of <i>Salmonella</i> Gallinarum in broiler chicks

description: An emerging highly pathogenic strain of <i>Salmonella</i> Gallinarum biovar Gallinarum (SG) was isolated from a severe natural outbreak of fowl typhoid, with enteric and systemic lesions, and used for the infection of broiler chicks. This strain was genetically characterized by a greater diversity of virulence and antimicrobial resistance genes. A total of 160 1-day-old Ross broiler chicks were divided into four groups and received oral and intranasal inoculations of SG at different cell concentrations. From 3-12 days post-inoculation, <i>postmortem</i> examinations were performed on the chicks to collect samples for bacteriological, histopathological, and immunohistochemical examinations. Typical gross and histological lesions such as fibrinonecrotic and/or histiocytic hepatitis, splenitis, and myocarditis were found in chicks from all groups. Unexpected lesions included marked transmural and non-transmural histiocytic enteritis, laryngitis and tracheitis, interstitial pneumonia and glomerulonephritis, glomerular thrombosis, fibrinous and histiocytic bone marrow myelitis, necrotic and histiocytic bursitis, and necrotic and histiocytic encephalitis. Within lesions, SG was detected by immunohistochemistry in all affected tissues, as well as by retrieval of the pathogen from liver and spleen in bacteriology. Intranasally challenged chicks had more pathogenic presentation of the disease changes, compared to orally challenged chicks, with a greater number of affected organs and intestinal segments. These results highlight the importance of the respiratory route for the pathogenesis of the infection and disease. An emerging highly virulent <i>S.</i> Gallinarum strain caused uncommon respiratory and intestinal lesions.The intranasal route of challenge caused a more severe and systemic disease, with broader organ involvement.The emerging strain targeted novel organs including larynx, trachea, bursa of Fabricius, and bone marrow. An emerging highly virulent <i>S.</i> Gallinarum strain caused uncommon respiratory and intestinal lesions. The intranasal route of challenge caused a more severe and systemic disease, with broader organ involvement. The emerging strain targeted novel organs including larynx, trachea, bursa of Fabricius, and bone marrow.
DOI: 10.34740/kaggle/ds/9270732
title: Global Food Insecurity Crisis Dataset (2017-2025)
DOI: 10.15468/dl.4jpc27
title: Occurrence Download

description: A dataset containing 20 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Kazakhstan",
 "Geometry POLYGON((34.35277 36.24421,70.64723 36.24421,70.64723 50.75579,34.35277 50.75579,34.35277 36.24421))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Crambe edentula"
 ]
}

 The dataset includes 20 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004683-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hsdnjg
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74391 24.46051, 88.66676699999999 24.454933, 88.591542 24.438341, 88.52010799999999 24.411146, 88.454235 24.374024, 88.395557 24.327898, 88.345519 24.273913, 88.305351 24.213406, 88.276032 24.147875, 88.25827 24.07894, 88.252486 24.008302, 88.25879999999999 23.937703, 88.27704 23.868878, 88.306738 23.803518, 88.34715 23.743227, 88.397272 23.689481, 88.455867 23.643594, 88.521495 23.606689, 88.59255 23.579665, 88.667297 23.563183, 88.74391 23.557644, 88.82052299999999 23.563183, 88.89527 23.579665, 88.966325 23.606689, 89.031953 23.643594, 89.090548 23.689481, 89.14067 23.743227, 89.181082 23.803518, 89.21078 23.868878, 89.22902000000001 23.937703, 89.23533399999999 24.008302, 89.22955 24.07894, 89.211788 24.147875, 89.182469 24.213406, 89.142301 24.273913, 89.092263 24.327898, 89.033585 24.374024, 88.96771200000001 24.411146, 88.896278 24.438341, 88.82105300000001 24.454933, 88.74391 24.46051))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004682-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcm4eg
title: Occurrence Download

description: A dataset listing the 47579 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Finland",
 "OccurrenceStatus is Present",
 "TaxonKey is Animalia"
 ]
}

 The dataset's 47579 records were derived from 818 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004659-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/gtnpmh6jzr.1
title: Terracing and mixed planting soil properties dataset

description: This dataset contains soil properties data of monoculture sloped farmlands, monoculture terraced farmlands, mixed planting terraced farmlands of Southwestern China.
DOI: 10.15468/dl.8ymruu
title: Occurrence Download

description: A dataset containing 921 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesembrinella bicolor"
 ]
}

 The dataset includes 921 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008550-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f2npwv
title: Occurrence Download

description: A dataset containing 3177 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hesperostipa spartea"
 ]
}

 The dataset includes 3177 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008551-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ag4bth
title: Occurrence Download

description: A dataset containing 157437 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ambrosia artemisiifolia"
 ]
}

 The dataset includes 157437 records from 872 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008537-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d7kts2
title: Occurrence Download

description: A dataset containing 551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.25796 10.44942,-73.25987 10.44919,-73.27466 10.43764,-73.27756 10.42454,-73.27198 10.40989,-73.2612 10.39774,-73.24265 10.39455,-73.23154 10.3978,-73.22252 10.40658,-73.2183 10.41928,-73.21907 10.43254,-73.22601 10.44289,-73.24359 10.45105,-73.24835 10.45265,-73.25796 10.44942))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 551 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008552-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2ubv2
title: Occurrence Download

description: A dataset containing 329 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mesembrinella randa"
 ]
}

 The dataset includes 329 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008553-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15472/wljpyv
title: Cladóceros de dos sistemas lénticos del departamento de Boyacá, Colombia

description: Este recurso presenta información sobre las comunidades de cladóceros asociados al Lago Sochagota (Paipa) y Lago de Tota (Aquitania, Tota) departamento de Boyacá, Colombia.Este recurso contiene 6 registros de cladóceros, que en su mayoría han sido identificados a nivel de género. Se siguen adelantando procesos de identificación y procesamiento de las muestras. Los especímenes registrados se agrupan en 1 clase, 1 orden y 4 familias.
DOI: 10.7910/dvn/mytkns
title: African Justice in Precarious Moments: Evolving approaches to remedial mandates at the African Court on Human and Peoples' Rights

description: Data for African Justice in Precarious Moments: Evolving approaches to remedial mandates at the African Court on Human and Peoples' Rights
DOI: 10.15468/dl.mzcd8q
title: Occurrence Download

description: A dataset containing 578 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Monocondylaea"
 ]
}

 The dataset includes 578 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008556-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17156051
title: CocoaMoniliaDataSet

description: Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning architectures. Digital image datasets serve as the main input for these architectures, enabling the analysis of patterns associated with a specific disease. However, some diseases have not yet been explored due to the limited availability of annotated image datasets. Cocoa pods are fundamental for the production of chocolate and its derived products; nevertheless, their production is threatened by Monilia Roreri, a fungal disease responsible for yield losses of approximately 30% - 40%. Therefore, this paper proposes a CocoaMoniliaSet, a dataset of cocoa pods labeled across symptomatic stages of Monilia disease. Although the infection of cocoa pod caused by Monilia roreri describes four biological cycles, the dataset takes the visual symptoms into three classes to support computer vision task. These symptoms correspond to: cycle 1 (humps), cycle 2 (oily and brown spots and cycle 3 (white powder or sporulation). In this paper, cycle 2 represents a symptomatic stage that merges the second and third biological cycles.In addition, the dataset included healthy cocoa pods to facilitate early detection of the disease. The dataset comprises 1953 images with four labeled classes: (1) healthy cocoa pod labeled as (h0); (2)first Monilia cycle, humps, labeled as (m1); (3)second and third Monilia cycle of the disease labeled as (m2); and (4) fourth Monilia cycle labeled as m3. Each instance in the image was annotated using the polygon method in CVAT (Computer Vision Annotation Tool), and the resulting labels are provided in COCO 1.0, YOLO, and segmentation mask 1.1 format to enable training object detection algorithms using bounding boxes. The publication of this dataset is essential for exploring techniques to diagnose cocoa disease using computer vision techniques.

The dataset is structured as follows:



CocoaMoniliaDataSet/

│

├── cocoapods_images/

│     ├── h0/

│     ├── m1/

│     ├── m2/

│     ├── m3/

│

│── COCO_annotations/

│     ├── instances_h0.json

│     ├── instances_m1.json

│     ├── instances_m2.json

│     ├── instances_m3.json

│

│── mask_segmentation/

│     ├── SegmentationClassh0/

│     ├── segmentationObjecth0/

│     ├── SegmentationClassm1/

│     ├── segmentationObjectm1/

│     ├── SegmentationClassm2/

│     ├── segmentationObjectm2/

│     ├── SegmentationClassm3/

│     ├── segmentationObjectm3/

│

│── DataSet_split_seg/

│     ├── images/

│     ├── labels/

│    

│── YOLO_annotations/

      ├── h0/

      ├── m1/

      ├── m2/

      ├── m3/

 

Repository link:  CocoaMoniliaDataSet

Notebook link: CocoaMoniliaDataSet.ipynb
DOI: 10.5281/zenodo.17716661
title: CocoaMoniliaDataSet

description: Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning architectures. Digital image datasets serve as the main input for these architectures, enabling the analysis of patterns associated with a specific disease. However, some diseases have not yet been explored due to the limited availability of annotated image datasets. Cocoa pods are fundamental for the production of chocolate and its derived products; nevertheless, their production is threatened by Monilia Roreri, a fungal disease responsible for yield losses of approximately 30% - 40%. Therefore, this paper proposes a CocoaMoniliaSet, a dataset of cocoa pods labeled across symptomatic stages of Monilia disease. Although the infection of cocoa pod caused by Monilia roreri describes four biological cycles, the dataset takes the visual symptoms into three classes to support computer vision task. These symptoms correspond to: cycle 1 (humps), cycle 2 (oily and brown spots and cycle 3 (white powder or sporulation). In this paper, cycle 2 represents a symptomatic stage that merges the second and third biological cycles.In addition, the dataset included healthy cocoa pods to facilitate early detection of the disease. The dataset comprises 1953 images with four labeled classes: (1) healthy cocoa pod labeled as (h0); (2)first Monilia cycle, humps, labeled as (m1); (3)second and third Monilia cycle of the disease labeled as (m2); and (4) fourth Monilia cycle labeled as m3. Each instance in the image was annotated using the polygon method in CVAT (Computer Vision Annotation Tool), and the resulting labels are provided in COCO 1.0, YOLO, and segmentation mask 1.1 format to enable training object detection algorithms using bounding boxes. The publication of this dataset is essential for exploring techniques to diagnose cocoa disease using computer vision techniques.

The dataset is structured as follows:



CocoaMoniliaDataSet/

│

├── cocoapods_images/

│     ├── h0/

│     ├── m1/

│     ├── m2/

│     ├── m3/

│

│── COCO_annotations/

│     ├── instances_h0.json

│     ├── instances_m1.json

│     ├── instances_m2.json

│     ├── instances_m3.json

│

│── mask_segmentation/

│     ├── SegmentationClassh0/

│     ├── segmentationObjecth0/

│     ├── SegmentationClassm1/

│     ├── segmentationObjectm1/

│     ├── SegmentationClassm2/

│     ├── segmentationObjectm2/

│     ├── SegmentationClassm3/

│     ├── segmentationObjectm3/

│

│── DataSet_split_seg/

│     ├── images/

│     ├── labels/

│    

│── YOLO_annotations/

      ├── h0/

      ├── m1/

      ├── m2/

      ├── m3/

 

Repository link:  CocoaMoniliaDataSet

Notebook link: CocoaMoniliaDataSet.ipynb
DOI: 10.57783/vjaa-7027
title: Indiana HCUP
DOI: 10.57783/0425-h602
title: Indiana HCUP
DOI: 10.6084/m9.figshare.30715733
title: Development of a pre-discharge model for 1-year post-discharge all-cause mortality after endovascular treatment for aneurysmal subarachnoid haemorrhage using LASSO–Boruta feature selection

description: To develop a predischarge model for predicting 1-year post-discharge all-cause mortality in patients with aneurysmal subarachnoid haemorrhage (aSAH) following endovascular treatment (EVT). We retrospectively analysed 947 patients with aSAH who were discharged alive between April 2021 and April 2023 from four neurointerventional centres in China as the training cohort. Candidate variables were selected using the least absolute shrinkage and selection operator (LASSO) combined with the Boruta algorithm. Based on these features, six models – logistic regression (LR), XGBoost, random forest (RF), AdaBoost, decision tree, and gradient boosting decision tree (GBDT) – were developed and compared. The optimal model was selected by the area under the receiver operating characteristic curve (AUC). The external validation cohort comprised 692 aSAH patients discharged alive between April 2023 and April 2024 from two additional centres. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA). Given the imbalanced outcome distribution, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to further assess model generalisability. Among 1,639 patients alive at discharge, 67 (4.1%) died within 1 year. LASSO and Boruta jointly identified five key predictors for model construction: age, modified World Federation of Neurosurgical Societies (mWFNS) grade, ICU length of stay (ICU-LOS), C-reactive protein (CRP), and monocyte-to-HDL ratio (MHR). The random forest achieved the best discrimination in training set and remained strong in external validation cohorts.Moreover, SMOTE training yielded further improvements in generalisability. Random forest model enables individualised pre-discharge risk stratification and may guide perioperative management.
DOI: 10.6084/m9.figshare.30715733.v1
title: Development of a pre-discharge model for 1-year post-discharge all-cause mortality after endovascular treatment for aneurysmal subarachnoid haemorrhage using LASSO–Boruta feature selection

description: To develop a predischarge model for predicting 1-year post-discharge all-cause mortality in patients with aneurysmal subarachnoid haemorrhage (aSAH) following endovascular treatment (EVT). We retrospectively analysed 947 patients with aSAH who were discharged alive between April 2021 and April 2023 from four neurointerventional centres in China as the training cohort. Candidate variables were selected using the least absolute shrinkage and selection operator (LASSO) combined with the Boruta algorithm. Based on these features, six models – logistic regression (LR), XGBoost, random forest (RF), AdaBoost, decision tree, and gradient boosting decision tree (GBDT) – were developed and compared. The optimal model was selected by the area under the receiver operating characteristic curve (AUC). The external validation cohort comprised 692 aSAH patients discharged alive between April 2023 and April 2024 from two additional centres. Model performance was evaluated using AUC, calibration curves, and decision curve analysis (DCA). Given the imbalanced outcome distribution, we applied the Synthetic Minority Over-sampling Technique (SMOTE) to further assess model generalisability. Among 1,639 patients alive at discharge, 67 (4.1%) died within 1 year. LASSO and Boruta jointly identified five key predictors for model construction: age, modified World Federation of Neurosurgical Societies (mWFNS) grade, ICU length of stay (ICU-LOS), C-reactive protein (CRP), and monocyte-to-HDL ratio (MHR). The random forest achieved the best discrimination in training set and remained strong in external validation cohorts.Moreover, SMOTE training yielded further improvements in generalisability. Random forest model enables individualised pre-discharge risk stratification and may guide perioperative management.
DOI: 10.15468/dl.nwvjs5
title: Occurrence Download

description: A dataset containing 112 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Souzalopesiella facialis"
 ]
}

 The dataset includes 112 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008557-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.