cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.43hnb4
title: Occurrence Download

description: A dataset containing 18101 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Aramus guarauna"
 ]
}

 The dataset includes 18101 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007822-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kt33yt
title: Occurrence Download

description: A dataset containing 118 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blakea schlimii",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 118 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007823-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/bfzv8dky89
title: 发乎情, 止乎虑？道德认知倾向对中学生欺凌旁观者行为的影响

description: The behavior of bystanders in adolescent bullying directly affects the continuation or cessation of bullying in schools. However, existing research often combines different types of bystander behavior, which may obscure the underlying psychological mechanisms behind these behaviors. This paper systematically examines the impact of moral cognitive tendencies on different types of bystander behavior in adolescent bullying through three studies (N = 947). (1) Study 1 found that individuals with moral identification were more likely to engage in proactive bystander behavior, while those with moral disengagement were more likely to engage in passive bystander behavior. (2) Studies 2 and 3, using causal chain experimental designs and mediating measurements, jointly demonstrate that moral identification predicts proactive bystander behavior through a partial mediation of empathy and positive outcome expectations. In contrast, moral disengagement predicts passive bystander behavior through a full mediation of negative outcome expectations and suppressed empathy. This research expands the perspective on bystander behavior in adolescent bullying, revealing the differentiated psychological pathways behind different types of bystander behavior. It provides valuable insights for constructing interventions against school bullying.
DOI: 10.17632/bfzv8dky89.1
title: 发乎情, 止乎虑？道德认知倾向对中学生欺凌旁观者行为的影响

description: The behavior of bystanders in adolescent bullying directly affects the continuation or cessation of bullying in schools. However, existing research often combines different types of bystander behavior, which may obscure the underlying psychological mechanisms behind these behaviors. This paper systematically examines the impact of moral cognitive tendencies on different types of bystander behavior in adolescent bullying through three studies (N = 947). (1) Study 1 found that individuals with moral identification were more likely to engage in proactive bystander behavior, while those with moral disengagement were more likely to engage in passive bystander behavior. (2) Studies 2 and 3, using causal chain experimental designs and mediating measurements, jointly demonstrate that moral identification predicts proactive bystander behavior through a partial mediation of empathy and positive outcome expectations. In contrast, moral disengagement predicts passive bystander behavior through a full mediation of negative outcome expectations and suppressed empathy. This research expands the perspective on bystander behavior in adolescent bullying, revealing the differentiated psychological pathways behind different types of bystander behavior. It provides valuable insights for constructing interventions against school bullying.
DOI: 10.17632/tt4p9sdvv5
title: Reliability comparison of multiple machine learning models considering spatial heterogeneity in landslide susceptibility assessment: a case study of Yaozhou district, China

description: Original data and model results.
DOI: 10.17632/tt4p9sdvv5.1
title: Reliability comparison of multiple machine learning models considering spatial heterogeneity in landslide susceptibility assessment: a case study of Yaozhou district, China

description: Original data and model results.
DOI: 10.17632/w3ffky29k6
title: Isotope of the North Plain Groundwater

description: This dataset is based on the research of groundwater circulation in the North China Plain. The dataset shows the oxygen isotope signature of groundwater in different aquifers. The results of the data show that there is inter-basin groundwater flow in the North China Plain.
DOI: 10.17632/w3ffky29k6.4
title: Isotope of the North Plain Groundwater

description: This dataset is based on the research of groundwater circulation in the North China Plain. The dataset shows the oxygen isotope signature of groundwater in different aquifers. The results of the data show that there is inter-basin groundwater flow in the North China Plain.
DOI: 10.17632/t3hdn3dtjz
title: Long non-coding RNA LINC00174 Acts as a miR-3713 Sponge to Deregulate Oncogenic Targets, Promoting Nasopharyngeal Carcinoma Xenograft Progression via Modulating Tumor Growth, Apoptosis, and Histopathology

description: Database Description: LINC00174-miR-3713 Axis in NPC
This database curates experimental data validating lncRNA LINC00174’s oncogenic role in nasopharyngeal carcinoma (NPC) via sponging miR-3713. It integrates molecular interaction, in vitro/in vivo functional assays, and histopathology data to support NPC biomarker discovery and targeted therapy research.

Core Data Modules
Molecular Interaction: LINC00174 siRNA sequence (5’-GGAUUCUAGACUUCUACUATT-3’); L-KD reduces LINC00174 by 76% and increases miR-3713 by 3.8-fold (p&lt;0.001).
In Vitro Assays: C666-1 cell experiments (4 groups: Vector NC, siRNA NC, L-KD, L-KD+I). L-KD downregulates Ki-67 (84%), PCNA (90%), MMP-2 (88%), and upregulates E-cadherin (7.3-fold; p&lt;0.001).
In Vivo Tumorigenesis: BALB/c-nu/nu mice xenografts. L-KD reduces tumor volume (63%) and weight (63%) on Day28; miR-3713 inhibition partially rescues these effects (p&lt;0.001).
Histopathology: L-KD reduces nuclear pleomorphism (H&E) and increases TUNEL⁺ apoptotic cells (3.6-fold; p&lt;0.001), reversed by miR-3713 inhibition.
Utility
Supports lncRNA-miRNA network analysis, NPC biomarker identification, and LINC00174-targeted therapy development.

Keywords: LINC00174; miR-3713; NPC; ceRNA; therapeutic target.
DOI: 10.17632/t3hdn3dtjz.1
title: Long non-coding RNA LINC00174 Acts as a miR-3713 Sponge to Deregulate Oncogenic Targets, Promoting Nasopharyngeal Carcinoma Xenograft Progression via Modulating Tumor Growth, Apoptosis, and Histopathology

description: Database Description: LINC00174-miR-3713 Axis in NPC
This database curates experimental data validating lncRNA LINC00174’s oncogenic role in nasopharyngeal carcinoma (NPC) via sponging miR-3713. It integrates molecular interaction, in vitro/in vivo functional assays, and histopathology data to support NPC biomarker discovery and targeted therapy research.

Core Data Modules
Molecular Interaction: LINC00174 siRNA sequence (5’-GGAUUCUAGACUUCUACUATT-3’); L-KD reduces LINC00174 by 76% and increases miR-3713 by 3.8-fold (p&lt;0.001).
In Vitro Assays: C666-1 cell experiments (4 groups: Vector NC, siRNA NC, L-KD, L-KD+I). L-KD downregulates Ki-67 (84%), PCNA (90%), MMP-2 (88%), and upregulates E-cadherin (7.3-fold; p&lt;0.001).
In Vivo Tumorigenesis: BALB/c-nu/nu mice xenografts. L-KD reduces tumor volume (63%) and weight (63%) on Day28; miR-3713 inhibition partially rescues these effects (p&lt;0.001).
Histopathology: L-KD reduces nuclear pleomorphism (H&E) and increases TUNEL⁺ apoptotic cells (3.6-fold; p&lt;0.001), reversed by miR-3713 inhibition.
Utility
Supports lncRNA-miRNA network analysis, NPC biomarker identification, and LINC00174-targeted therapy development.

Keywords: LINC00174; miR-3713; NPC; ceRNA; therapeutic target.
DOI: 10.17632/7h8r9b9vdw
title: The Effect of Visualization Modes about Mathematical Word Problems Scenario on Problem-Solving Performance

description: Three sets of PPT presentation scenarios were designed to present different problem situations. three experimental groups to undergo eye movement experiments: static abstract diagram group (SAD), n = 31; static situational diagram group (SSD), n = 31; dynamic situational diagram group (DSD), n = 32. The collected data describe various eye-tracking indicators of students’ gazes under different scenario presentation groups, as well as their performance, delayed post-test, answer levles ,cognitive load, and learning willing before and after the experiment.
DOI: 10.17632/7h8r9b9vdw.1
title: The Effect of Visualization Modes about Mathematical Word Problems Scenario on Problem-Solving Performance

description: Three sets of PPT presentation scenarios were designed to present different problem situations. three experimental groups to undergo eye movement experiments: static abstract diagram group (SAD), n = 31; static situational diagram group (SSD), n = 31; dynamic situational diagram group (DSD), n = 32. The collected data describe various eye-tracking indicators of students’ gazes under different scenario presentation groups, as well as their performance, delayed post-test, answer levles ,cognitive load, and learning willing before and after the experiment.
DOI: 10.17632/k98pt82n8f
title: Data for zixing Precipitation data

description: Precipitation data for stations Yuandong and Qiumu。
DOI: 10.17632/k98pt82n8f.1
title: Data for zixing Precipitation data

description: Precipitation data for stations Yuandong and Qiumu。
DOI: 10.17632/srjnn94fw3
title: Yasuharu_River_raw_data

description: This is the data for the paper titled “Action research to discourage littering around river: The effect of intervention using public art drawn by children.”
DOI: 10.17632/srjnn94fw3.1
title: Yasuharu_River_raw_data

description: This is the data for the paper titled “Action research to discourage littering around river: The effect of intervention using public art drawn by children.”
DOI: 10.17632/mc9n3gpw38
title: BD-FakeDetect: A Bangla Misinformation Dataset

description: BD-FakeDetect is a manually curated Bangla misinformation dataset of 5,929 news/posts from trusted fact-checkers (Rumor Scanner, FactWatch, BOOM Bangladesh, Jachai) with rich text+image metadata. Each entry provides title/content/source/date/claim-type/context/final-decision/summary, an 8-class label (False, Misleading, Altered, Scam, Satire, True, Partially True/False), and image paths for multimodal research.
DOI: 10.15468/dl.ar736c
title: Occurrence Download

description: A dataset containing 9551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Crepis aurea, Crepis aurea, Crepis aurea)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 9551 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007825-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/mc9n3gpw38.2
title: BD-FakeDetect: A Bangla Misinformation Dataset

description: BD-FakeDetect is a manually curated Bangla misinformation dataset of 5,929 news/posts from trusted fact-checkers (Rumor Scanner, FactWatch, BOOM Bangladesh, Jachai) with rich text+image metadata. Each entry provides title/content/source/date/claim-type/context/final-decision/summary, an 8-class label (False, Misleading, Altered, Scam, Satire, True, Partially True/False), and image paths for multimodal research.
DOI: 10.17632/s2sbrg24mw
title: Data GenAI

description: This is data of article "From GenAI Stimulus to Productivity Response: The Mediating Role of Employee Work Motivation in Vietnam."
DOI: 10.17632/s2sbrg24mw.1
title: Data GenAI

description: This is data of article "From GenAI Stimulus to Productivity Response: The Mediating Role of Employee Work Motivation in Vietnam."
DOI: 10.17632/ymt8k9bjkn
title: Image Dataset on Chili Leaf Diseases in the Krishna River Basin of the Deccan Plateau, India

description: Overview
This dataset is a comprehensive resource for researchers and professionals in agriculture, machine learning, and computer vision, focusing on chili plant disease detection and growth stage classification. It provides high-resolution images of both healthy and diseased chili leaves, as well as samples representing various growth stages, making it highly suitable for deep learning and AI-based precision agriculture applications.
Data Collection
The images were systematically collected between October 2024 and February 2025 from chili plantations in the Bellary and Raichur districts of Karnataka, and the Prathipadu, Vatticherukuru, and Medikonduru of Guntur and Parchur, Yeddana Pudi, and MarturPrakasam districts of Andhra Pradesh, which are recognized as the global epicenters for red chili production. All field collection was conducted under the direct supervision of agricultural experts to ensure the pathological accuracy of the healthy and diseased leaf samples. The collection effort spanned across diverse agro-climatic zones of the Krishna River Basin within the Deccan Plateau.
This multi-regional coverage ensured that a wide range of environmental and cultivation conditions were represented, capturing real-world variability in chili plant health, disease manifestation, and growth patterns.
Using advanced digital cameras, high-resolution images were gathered, documenting different disease types and developmental stages to support robust AI-driven agricultural research.
Dataset Structure
The dataset is organized into two primary categories:

Chili Leaf Disease Dataset
This category includes images depicting six major leaf diseases affecting chili plants. Each image is meticulously labeled and verified by agricultural experts to enable precise classification and model training.

Original Dataset: 1,856 high-resolution images (.jpg)
Augmented Dataset: 12,000 enhanced images generated through data augmentation techniques (rotation, flipping, contrast adjustment, and zoom)
# Disease Classes Covered:
Bacterial Spot

Curl Virus

Cercospora Leaf Spot

Nutrition Deficiency

White Spot

Healthy Leaves
Specifically, this dataset enables:
✅ Early and Accurate Disease Detection — facilitating the identification of key foliar diseases such as Bacterial Spot, Curl Virus and Cercospora Leaf Spot at early stages, thereby preventing large-scale crop losses and improving disease management efficiency.
✅ Automated Growth Stage Monitoring — providing visual evidence of chili plants at various growth stages, enabling automated phenotyping, yield estimation and adaptive farm management strategies using AI and computer vision models.
✅ Benchmark for AI and Computer Vision Models — serving as a standardized dataset for training and benchmarking deep learning architectures such as CNNs, Vision Transformers and hybrid models for plant disease recognition, segmentation, and classification tasks.
DOI: 10.17632/ymt8k9bjkn.3
title: Image Dataset on Chili Leaf Diseases in the Krishna River Basin of the Deccan Plateau, India

description: Overview
This dataset is a comprehensive resource for researchers and professionals in agriculture, machine learning, and computer vision, focusing on chili plant disease detection and growth stage classification. It provides high-resolution images of both healthy and diseased chili leaves, as well as samples representing various growth stages, making it highly suitable for deep learning and AI-based precision agriculture applications.
Data Collection
The images were systematically collected between October 2024 and February 2025 from chili plantations in the Bellary and Raichur districts of Karnataka, and the Prathipadu, Vatticherukuru, and Medikonduru of Guntur and Parchur, Yeddana Pudi, and MarturPrakasam districts of Andhra Pradesh, which are recognized as the global epicenters for red chili production. All field collection was conducted under the direct supervision of agricultural experts to ensure the pathological accuracy of the healthy and diseased leaf samples. The collection effort spanned across diverse agro-climatic zones of the Krishna River Basin within the Deccan Plateau.
This multi-regional coverage ensured that a wide range of environmental and cultivation conditions were represented, capturing real-world variability in chili plant health, disease manifestation, and growth patterns.
Using advanced digital cameras, high-resolution images were gathered, documenting different disease types and developmental stages to support robust AI-driven agricultural research.
Dataset Structure
The dataset is organized into two primary categories:

Chili Leaf Disease Dataset
This category includes images depicting six major leaf diseases affecting chili plants. Each image is meticulously labeled and verified by agricultural experts to enable precise classification and model training.

Original Dataset: 1,856 high-resolution images (.jpg)
Augmented Dataset: 12,000 enhanced images generated through data augmentation techniques (rotation, flipping, contrast adjustment, and zoom)
# Disease Classes Covered:
Bacterial Spot

Curl Virus

Cercospora Leaf Spot

Nutrition Deficiency

White Spot

Healthy Leaves
Specifically, this dataset enables:
✅ Early and Accurate Disease Detection — facilitating the identification of key foliar diseases such as Bacterial Spot, Curl Virus and Cercospora Leaf Spot at early stages, thereby preventing large-scale crop losses and improving disease management efficiency.
✅ Automated Growth Stage Monitoring — providing visual evidence of chili plants at various growth stages, enabling automated phenotyping, yield estimation and adaptive farm management strategies using AI and computer vision models.
✅ Benchmark for AI and Computer Vision Models — serving as a standardized dataset for training and benchmarking deep learning architectures such as CNNs, Vision Transformers and hybrid models for plant disease recognition, segmentation, and classification tasks.
DOI: 10.17909/k7kg-s485
title: AU Mic TESS Cycle 1 and 3

description: TESS Cycle 1 and Cycle 3 observations of AU Mic.
DOI: 10.17632/rs5gs95x45
title: Luffa_Leaf_Disease_Dataset

description: 1. Content: Luffa leaf images
2. Format of images: JPG
3. Number of Class: 6
4. Total Number of Images: 1,342
5. Folder Structure: Provided in the readme file.
DOI: 10.17632/rs5gs95x45.1
title: Luffa_Leaf_Disease_Dataset

description: 1. Content: Luffa leaf images
2. Format of images: JPG
3. Number of Class: 6
4. Total Number of Images: 1,342
5. Folder Structure: Provided in the readme file.
DOI: 10.15468/dl.squqhj
title: Occurrence Download

description: A dataset containing 30365 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Crepis biennis, Crepis biennis, Crepis biennis, Crepis biennis, Crepis biennis)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 30365 records from 78 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007826-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fwpwfj
title: Occurrence Download

description: A dataset containing 633 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Crepis blattarioides, Crepis blattarioides, Crepis conyzifolia, Crepis conyzifolia)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 633 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007827-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tbvkxkdgsy
title: Dataset1 for "Alphaviral nonstructural protein-host RBP co-condensation as a mechanism to sustain virus replication"
DOI: 10.17632/tbvkxkdgsy.1
title: Dataset1 for "Alphaviral nonstructural protein-host RBP co-condensation as a mechanism to sustain virus replication"
DOI: 10.15468/dl.v7wunj
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Blastus borneensis",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 85 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007828-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/ygfsjkvbv4
title: Dataset2 for Alphaviral nonstructural protein-host RBP co-condensation as a mechanism to sustain virus replication
DOI: 10.17632/ygfsjkvbv4.1
title: Dataset2 for Alphaviral nonstructural protein-host RBP co-condensation as a mechanism to sustain virus replication
DOI: 10.17632/y7dkrpv7g9
title: Deep Learning-Based Detection and Anatomical Classification of Retained Roots and Periodontally Compromised Teeth in Orthopantomogram across Multi-National Populations

description: The purpose of this study was to develop and evaluate deep learning models for the automated detection of retained roots in panoramic radiographs, with a specific focus on comparing oriented bounding
box (OBB) and axis-aligned bounding box (AABB) annotation strategies across diverse populations.A multinational dataset of 4,768 panoramic radiographs was annotated into seven clinically
relevant classes using both oriented bounding boxes (OBB) and axis-aligned bounding boxes (AABB) approaches. You Only Look Once version 11 (YOLOv11) and Real-Time Detection Transformer (RT-DETR) models were trained under identical conditions and evaluated using mean average precision (mAP) and loss metrics. Inference results were further examined through qualitative case analysis to capture strengths and
limitations in clinically challenging scenarios.
DOI: 10.17632/y7dkrpv7g9.2
title: Deep Learning-Based Detection and Anatomical Classification of Retained Roots and Periodontally Compromised Teeth in Orthopantomogram across Multi-National Populations

description: The purpose of this study was to develop and evaluate deep learning models for the automated detection of retained roots in panoramic radiographs, with a specific focus on comparing oriented bounding
box (OBB) and axis-aligned bounding box (AABB) annotation strategies across diverse populations.A multinational dataset of 4,768 panoramic radiographs was annotated into seven clinically
relevant classes using both oriented bounding boxes (OBB) and axis-aligned bounding boxes (AABB) approaches. You Only Look Once version 11 (YOLOv11) and Real-Time Detection Transformer (RT-DETR) models were trained under identical conditions and evaluated using mean average precision (mAP) and loss metrics. Inference results were further examined through qualitative case analysis to capture strengths and
limitations in clinically challenging scenarios.
DOI: 10.57967/hf/7709
title: AI_RedPill