cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/gphbs7vsbz.2
title: A Context-Aware Bengali Toxic Comments Dataset

description: This dataset provides a context-aware collection of Bengali toxic and non-toxic comments curated from the official Facebook pages of two leading Bangladeshi news portals, Prothom Alo and News24, during the period April–June 2025. All data were obtained from publicly available sources, ensuring that no private or personally identifiable information was collected or retained. In total, 1,004 comments were collected, covering diverse domains such as politics, sports, entertainment, and social issues. 

Unlike conventional toxic comment datasets, this dataset consists of several key attributes, including the news title, which represents the headline of the source article; metadata, which contains the detailed text of the corresponding news article; the target comment, which is the primary comment being analyzed; the predecessor comment, referring to the immediate parent comment where available; and the successor comment, which is the next comment in the thread. Additionally, the dataset retains the user name, representing the display name of the commenter, and a label assigned through majority voting to categorize each entry as either toxic or non-toxic.

Regarding class distribution, the dataset contains 507 toxic and 497 non-toxic samples, resulting in a nearly balanced composition. Toxic comments are defined as those containing hate speech, offensive or abusive language, sarcasm, personal attacks, or threats. For example, one toxic comment states: “শালা জানোয়ার টারে একদম ঠিকমতো বাটাম দেওয়া হোক,” which translates to “That bastard should be properly beaten.” Non-toxic comments, by contrast, are neutral, informative, constructive, or polite in nature, without harmful expressions. For instance, one non-toxic comment states: “চরিত্র খারাপ মনে হয় না, তবে বদমেজাজি মনে হয়, রাগ কন্ট্রোল করতে পারে না,” translated as “Doesn't seem to have a bad character, but appears short-tempered and cannot control anger.”
DOI: 10.17632/mmgrwcgxvw.1
title: Bibliographic Data for Blockchain-Based Smart Contracts in Agricultural Supply Chain

description: The data offers a collection of article datasets exploring the use of blockchain technology and smart contracts in enhancing agricultural logistics. It includes research on automation, traceability, fraud reduction, and operational efficiency within supply chains, focusing on the integration of blockchain with IoT for real-time monitoring.
DOI: 10.17632/5ky27g7db6
title: Data for 'Role of Critical Sets in the Permeability of Random Fracture Networks: A Percolation Scaling Perspective'

description: Data for 'Role of Critical Sets in the Permeability of Random Fracture Networks: A Percolation Scaling Perspective'
DOI: 10.17632/5ky27g7db6.1
title: Data for 'Role of Critical Sets in the Permeability of Random Fracture Networks: A Percolation Scaling Perspective'

description: Data for 'Role of Critical Sets in the Permeability of Random Fracture Networks: A Percolation Scaling Perspective'
DOI: 10.5281/zenodo.17015294
title: DeepSCFold: High-accuracy protein complex structure modeling based on sequence-derived structure complementarity
DOI: 10.5281/zenodo.17015295
title: DeepSCFold: High-accuracy protein complex structure modeling based on sequence-derived structure complementarity
DOI: 10.17632/b7pjz5wrvg
title: HK_multiproxy_Dataset

description: This research from Central Ganga Plain, indicates that fluctuations in lake levels and sediment characteristics over the past ~6,350 years are directly related to changes in precipitation and monsoon activity, which can be reconstructed through physical and geochemical proxies. The core premise of this study is that precipitation patterns, particularly the intensity of the Indian Summer Monsoon (ISM), have been the primary driver of lake-level fluctuations and sediment deposition changes in the Central Ganga Plain since the aforementioned time. For this analysis, Sediment samples were collected by excavating a ~91 cm trench from the Karela lake's periphery, where a total of 70 samples were retrieved at regular intervals along the trench profile. A multi-proxy approach was employed to achieve the study’s objective, incorporating sediment characteristics (including grain-size analysis and end-member modeling), mineral magnetism, CHNS analysis, and AMS radiocarbon dating for chronology. The findings highlight finer end-members in the silt domain (EM1, EM2) that reflect higher surface runoff and increased precipitation, corresponding to periods of intensified ISM. While coarser end-members in the fine sand domain (EM3, EM4) mark dry intervals coinciding with periods of droughts and monsoon minima in the Kanwar Lake record from the Central Ganga Plain (Phartiyal et al., 2024; DOI: https://doi.org/10.1016/j.quascirev.2024.108760), and reduced upwelling record of the Arabian Sea (Gupta et al., 2003; DOI: https://doi.org/10.1038/nature01340). The study’s chronological framework establishes links between regional lake-level records, ISM changes, and global Holocene climatic events. Grain-size and magnetic proxies, coupled with radiocarbon dating, form robust method for interpreting past monsoonal dynamics. Ultimately, this dataset provides a high-resolution window into Late Holocene climatic variability, demonstrating direct regional impacts of monsoon fluctuations and offering a framework for future paleoclimatic research in the region.
DOI: 10.17632/b7pjz5wrvg.1
title: HK_multiproxy_Dataset

description: This research from Central Ganga Plain, indicates that fluctuations in lake levels and sediment characteristics over the past ~6,350 years are directly related to changes in precipitation and monsoon activity, which can be reconstructed through physical and geochemical proxies. The core premise of this study is that precipitation patterns, particularly the intensity of the Indian Summer Monsoon (ISM), have been the primary driver of lake-level fluctuations and sediment deposition changes in the Central Ganga Plain since the aforementioned time. For this analysis, Sediment samples were collected by excavating a ~91 cm trench from the Karela lake's periphery, where a total of 70 samples were retrieved at regular intervals along the trench profile. A multi-proxy approach was employed to achieve the study’s objective, incorporating sediment characteristics (including grain-size analysis and end-member modeling), mineral magnetism, CHNS analysis, and AMS radiocarbon dating for chronology. The findings highlight finer end-members in the silt domain (EM1, EM2) that reflect higher surface runoff and increased precipitation, corresponding to periods of intensified ISM. While coarser end-members in the fine sand domain (EM3, EM4) mark dry intervals coinciding with periods of droughts and monsoon minima in the Kanwar Lake record from the Central Ganga Plain (Phartiyal et al., 2024; DOI: https://doi.org/10.1016/j.quascirev.2024.108760), and reduced upwelling record of the Arabian Sea (Gupta et al., 2003; DOI: https://doi.org/10.1038/nature01340). The study’s chronological framework establishes links between regional lake-level records, ISM changes, and global Holocene climatic events. Grain-size and magnetic proxies, coupled with radiocarbon dating, form robust method for interpreting past monsoonal dynamics. Ultimately, this dataset provides a high-resolution window into Late Holocene climatic variability, demonstrating direct regional impacts of monsoon fluctuations and offering a framework for future paleoclimatic research in the region.
DOI: 10.17632/v94bpk7h52
title: Supplemental Figure 1

description: Correlation between combination analysis of human leukocyte antigen typing (HLA)/ lymphocyte transformation test (LTT)/ enzyme-linked immunospot assay (ELISpot) and Naranjo adverse drug reaction scale score for antiepileptic drugs. The composite scoring criteria are allocated for each of the following: (1) detection of an antiepileptic drug-associated genetic risk variant, such as HLA-B*15:02, HLA-A*31:01 and HLA-B*24:02, (2) a positive ELISpot response, and (3) a positive LTT result. Data were represented as a rank for each individual. Statistical significance was determined by Spearman's rank correlation test.
DOI: 10.17632/v94bpk7h52.1
title: Supplemental Figure 1

description: Correlation between combination analysis of human leukocyte antigen typing (HLA)/ lymphocyte transformation test (LTT)/ enzyme-linked immunospot assay (ELISpot) and Naranjo adverse drug reaction scale score for antiepileptic drugs. The composite scoring criteria are allocated for each of the following: (1) detection of an antiepileptic drug-associated genetic risk variant, such as HLA-B*15:02, HLA-A*31:01 and HLA-B*24:02, (2) a positive ELISpot response, and (3) a positive LTT result. Data were represented as a rank for each individual. Statistical significance was determined by Spearman's rank correlation test.
DOI: 10.17632/nntnnzffw4
title: Vehicle License Plate Detection Dataset

description: Vehicle License Plate Detection Dataset
This dataset consists of high-resolution images of vehicle license plates captured in various real-world settings. The data was collected across different locations in Dhaka, Bangladesh, during the months of October and November 2024. The primary goal of this dataset is to enable research and development in vehicle license plate recognition systems using image processing and machine learning techniques.
Key Details:
Number of Images:2947
Number of Classes: 5 (types of license plates based on vehicle categories or regional variations)
Captured Locations: Dhaka, Ashulia, Narsingdi, Bangladesh
Capture Period: October 20 - November 2, 2024
Purpose:
Support the development of automated vehicle license plate recognition (LPR) systems.
Aid in real-time vehicle identification for security, parking management, and traffic monitoring systems.
Enable the creation of reliable image-based vehicle recognition models using deep learning algorithms.
Contribute to improved traffic regulation and law enforcement, facilitating easier identification of vehicles in urban environments.
DOI: 10.17632/nntnnzffw4.3
title: Vehicle License Plate Detection Dataset

description: Vehicle License Plate Detection Dataset
This dataset consists of high-resolution images of vehicle license plates captured in various real-world settings. The data was collected across different locations in Dhaka, Bangladesh, during the months of October and November 2024. The primary goal of this dataset is to enable research and development in vehicle license plate recognition systems using image processing and machine learning techniques.
Key Details:
Number of Images:2947
Number of Classes: 5 (types of license plates based on vehicle categories or regional variations)
Captured Locations: Dhaka, Ashulia, Narsingdi, Bangladesh
Capture Period: October 20 - November 2, 2024
Purpose:
Support the development of automated vehicle license plate recognition (LPR) systems.
Aid in real-time vehicle identification for security, parking management, and traffic monitoring systems.
Enable the creation of reliable image-based vehicle recognition models using deep learning algorithms.
Contribute to improved traffic regulation and law enforcement, facilitating easier identification of vehicles in urban environments.
DOI: 10.17632/tvy8y9dfxf
title: Supplementary Data for [Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.]

description: This dataset contains supplementary files related to the manuscript entitled 'Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.' It includes raw body weights, neurodevelopmental milestone acquisition data, statistical model outputs, and standardized composite scores.
DOI: 10.17632/tvy8y9dfxf.1
title: Supplementary Data for [Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.]

description: This dataset contains supplementary files related to the manuscript entitled 'Environmental enrichment partially rescues neurodevelopmental milestone delays in the prenatal VPA rat model of ASD.' It includes raw body weights, neurodevelopmental milestone acquisition data, statistical model outputs, and standardized composite scores.
DOI: 10.17632/5cx5mh3dn6
title: OFPV+WEC

description: data for performance of OFPV and WEC
DOI: 10.17632/5cx5mh3dn6.1
title: OFPV+WEC

description: data for performance of OFPV and WEC
DOI: 10.17632/t5xkbsnrgw
title: THE ACADEMIC INTEGRITY OF THE PROFESSOR: THE PATH TOWARDS A POSITIVE REPUTATION

description: This data set was used to perform regression analysis and suggest that the academic integrity of postgraduate professors at a university campus in central Mexico is a predictor variable of a positive reputation of said professors. The manuscript citation is as follows: Cabrera-Gala, R., & Cavazos Arroyo, J. (2023). La integridad académica del profesor: el camino hacia una reputación positiva [Professor's Academic Integrity: The Path To A Positive Reputation]. A&Amp;H Revista De Artes, Humanidades Y Ciencias Sociales, (17), 50–82. Retreived from https://revistas.upaep.mx/index.php/ayh/article/view/293
DOI: 10.17632/t5xkbsnrgw.2
title: THE ACADEMIC INTEGRITY OF THE PROFESSOR: THE PATH TOWARDS A POSITIVE REPUTATION

description: This data set was used to perform regression analysis and suggest that the academic integrity of postgraduate professors at a university campus in central Mexico is a predictor variable of a positive reputation of said professors. The manuscript citation is as follows: Cabrera-Gala, R., & Cavazos Arroyo, J. (2023). La integridad académica del profesor: el camino hacia una reputación positiva [Professor's Academic Integrity: The Path To A Positive Reputation]. A&Amp;H Revista De Artes, Humanidades Y Ciencias Sociales, (17), 50–82. Retreived from https://revistas.upaep.mx/index.php/ayh/article/view/293
DOI: 10.17632/c64jt5gkzm
title: Applying Convolutional Neural Networks for Early Detection of Diseases in Sesame Leaf

description: The Sesame Leaf Disease Dataset consists of 3,540 high-quality images, collected from sesame cultivation fields in Pabna District, Bangladesh. Images were captured under natural field conditions using high-resolution smartphone cameras to ensure clarity of disease symptoms. The dataset is organized into four major classes of sesame leaves.

Data Collection Details:
Captured Using:
1. Realme 8 (64 MP, f/1.79 aperture)
2. Redmi Note 12 Pro (50MP, f/1.79 aperture)

Data Source Location:
1. Pabna District, Bangladesh (Latitude: 24.006355, Longitude: 89.237202)

Number of Images (Original 3,540):

1. Healthy: 1,120
2. Insect Damage: 880
3. Leaf Spot: 790
4. Powdery Mildew: 750

Sesame Leaf Dataset Distribution

1. Healthy Leaf – Original Images: 1,335, Augmented Images: 3,000
2. Leaf Spot Disease – Original Images: 587, Augmented Images: 3,000
3. Yellowing Leaf Syndrome – Original Images: 894, Augmented Images: 3,000
4. Insect Leaf Damage – Original Images: 724, Augmented Images: 3,000
Total: Original = 3,540, Augmented = 12,000

Key Applications of the Sesame Leaf Disease Dataset

Early Disease Detection: Leaf images help train AI models to detect sesame diseases at an early stage, reducing crop loss.
Precision Agriculture: Farmers can use mobile or IoT-based apps powered by trained models to get instant disease diagnosis in the field.
Decision Support Systems: Assists agricultural experts and policymakers in monitoring disease spread and planning effective management strategies.
Smart Farming Tools: Can be integrated into drones and smart cameras for large-scale sesame field surveillance.
Educational and Research Use: Acts as a benchmark dataset for computer vision, deep learning, and plant pathology research.
Model Benchmarking: Useful for testing and comparing performance of different CNN architectures and transfer learning models.
Scalable to Other Crops: The methodology can be extended to detect diseases in other plants, ensuring wider applications in agriculture.
DOI: 10.17632/c64jt5gkzm.1
title: Applying Convolutional Neural Networks for Early Detection of Diseases in Sesame Leaf

description: The Sesame Leaf Disease Dataset consists of 3,540 high-quality images, collected from sesame cultivation fields in Pabna District, Bangladesh. Images were captured under natural field conditions using high-resolution smartphone cameras to ensure clarity of disease symptoms. The dataset is organized into four major classes of sesame leaves.

Data Collection Details:
Captured Using:
1. Realme 8 (64 MP, f/1.79 aperture)
2. Redmi Note 12 Pro (50MP, f/1.79 aperture)

Data Source Location:
1. Pabna District, Bangladesh (Latitude: 24.006355, Longitude: 89.237202)

Number of Images (Original 3,540):

1. Healthy: 1,120
2. Insect Damage: 880
3. Leaf Spot: 790
4. Powdery Mildew: 750

Sesame Leaf Dataset Distribution

1. Healthy Leaf – Original Images: 1,335, Augmented Images: 3,000
2. Leaf Spot Disease – Original Images: 587, Augmented Images: 3,000
3. Yellowing Leaf Syndrome – Original Images: 894, Augmented Images: 3,000
4. Insect Leaf Damage – Original Images: 724, Augmented Images: 3,000
Total: Original = 3,540, Augmented = 12,000

Key Applications of the Sesame Leaf Disease Dataset

Early Disease Detection: Leaf images help train AI models to detect sesame diseases at an early stage, reducing crop loss.
Precision Agriculture: Farmers can use mobile or IoT-based apps powered by trained models to get instant disease diagnosis in the field.
Decision Support Systems: Assists agricultural experts and policymakers in monitoring disease spread and planning effective management strategies.
Smart Farming Tools: Can be integrated into drones and smart cameras for large-scale sesame field surveillance.
Educational and Research Use: Acts as a benchmark dataset for computer vision, deep learning, and plant pathology research.
Model Benchmarking: Useful for testing and comparing performance of different CNN architectures and transfer learning models.
Scalable to Other Crops: The methodology can be extended to detect diseases in other plants, ensuring wider applications in agriculture.
DOI: 10.17632/5tc5nmx5wy
title: Sustainable Solid Waste Management Data Set

description: he dataset was developed to examine the key factors influencing sustainable solid waste management (SWM) in Peshawar, Pakistan. It is based on a structured questionnaire derived from literature review and expert consultation, covering three latent constructs: Sustainability Factors (SSWM), Stakeholder Engagement (SE), and Solid Waste Management Strategies (SWMS).
A total of 420 valid responses were collected from diverse stakeholders, including municipal experts, academics, NGO representatives, and the general public. Responses were measured on a five-point Likert scale (1 = very low to 5 = very high), alongside demographic and professional details.
DOI: 10.17632/5tc5nmx5wy.1
title: Sustainable Solid Waste Management Data Set

description: he dataset was developed to examine the key factors influencing sustainable solid waste management (SWM) in Peshawar, Pakistan. It is based on a structured questionnaire derived from literature review and expert consultation, covering three latent constructs: Sustainability Factors (SSWM), Stakeholder Engagement (SE), and Solid Waste Management Strategies (SWMS).
A total of 420 valid responses were collected from diverse stakeholders, including municipal experts, academics, NGO representatives, and the general public. Responses were measured on a five-point Likert scale (1 = very low to 5 = very high), alongside demographic and professional details.
DOI: 10.17632/wmpnympwkz
title: Deep Learning for Precision Agriculture: Diagnosing Jujube Leaf Diseases via Image Classification

description: he Jujube Leaf Disease Dataset comprises 2,819 high-quality images, categorized into four distinct classes representing different conditions of jujube leaves. This dataset is curated to support machine learning, deep learning, and computer vision applications for automated leaf disease recognition and classification.

Each image was collected under natural field conditions and captures clear visual symptoms of the leaf’s health status. This ensures effective feature extraction, model training, and validation, making the dataset a valuable resource for agricultural AI applications.

Data Collection Details:
Captured Using:
1. Realme 8 (64 MP, f/1.79 aperture)
2. Redmi Note 10 Pro Max (48 MP, f/1.79 aperture)

Images were collected from Gazipur, Bangladesh at the following locations:

1. Kalameshwar Dakshinpara, Board Bazar, Gazipur (Latitude: 23.941363, Longitude: 90.381985)
2. Kathora, Board Bazar, Gazipur (Latitude: 23.944901, Longitude: 90.365987)

Number of Images: 
 1. Healthy 1340 
 2. Insect Damage 750 
 3. Leaf Spot 729 
 Total 2819

Dataset Distribution

1. Healthy Leaf – Original: 1,340 - Augmented: 3,000
2. Insect Damage – Original: 750 - Augmented: 3,000
3. Leaf Spot Disease – Original: 729 - Augmented: 3,000
Total: Original = 2,819 - Augmented = 9,000

Key Applications of the Jujube Leaf Disease Dataset

Early Disease Detection – Enables AI models to identify jujube leaf diseases at an early stage, reducing crop loss.
Precision Agriculture – Supports mobile or IoT-based apps for instant disease diagnosis in the field.
Decision Support Systems – Assists agricultural experts and policymakers in monitoring disease spread and planning management strategies.
Smart Farming Tools – Can be integrated with drones and smart cameras for large-scale field surveillance.
Educational & Research Use – Acts as a benchmark dataset for deep learning, computer vision, and plant pathology research.
Model Benchmarking – Useful for testing and comparing performance of different CNN architectures and transfer learning models.
DOI: 10.17632/wmpnympwkz.1
title: Deep Learning for Precision Agriculture: Diagnosing Jujube Leaf Diseases via Image Classification

description: he Jujube Leaf Disease Dataset comprises 2,819 high-quality images, categorized into four distinct classes representing different conditions of jujube leaves. This dataset is curated to support machine learning, deep learning, and computer vision applications for automated leaf disease recognition and classification.

Each image was collected under natural field conditions and captures clear visual symptoms of the leaf’s health status. This ensures effective feature extraction, model training, and validation, making the dataset a valuable resource for agricultural AI applications.

Data Collection Details:
Captured Using:
1. Realme 8 (64 MP, f/1.79 aperture)
2. Redmi Note 10 Pro Max (48 MP, f/1.79 aperture)

Images were collected from Gazipur, Bangladesh at the following locations:

1. Kalameshwar Dakshinpara, Board Bazar, Gazipur (Latitude: 23.941363, Longitude: 90.381985)
2. Kathora, Board Bazar, Gazipur (Latitude: 23.944901, Longitude: 90.365987)

Number of Images: 
 1. Healthy 1340 
 2. Insect Damage 750 
 3. Leaf Spot 729 
 Total 2819

Dataset Distribution

1. Healthy Leaf – Original: 1,340 - Augmented: 3,000
2. Insect Damage – Original: 750 - Augmented: 3,000
3. Leaf Spot Disease – Original: 729 - Augmented: 3,000
Total: Original = 2,819 - Augmented = 9,000

Key Applications of the Jujube Leaf Disease Dataset

Early Disease Detection – Enables AI models to identify jujube leaf diseases at an early stage, reducing crop loss.
Precision Agriculture – Supports mobile or IoT-based apps for instant disease diagnosis in the field.
Decision Support Systems – Assists agricultural experts and policymakers in monitoring disease spread and planning management strategies.
Smart Farming Tools – Can be integrated with drones and smart cameras for large-scale field surveillance.
Educational & Research Use – Acts as a benchmark dataset for deep learning, computer vision, and plant pathology research.
Model Benchmarking – Useful for testing and comparing performance of different CNN architectures and transfer learning models.