cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/nnv3k3m94k
title: Pisum sativum Image Dataset: Healthy and Disease-Affected Cases

description: Early and accurate detection of plant diseases is vital for sustainable agriculture and optimizing crop yields. This dataset, titled the Pisum sativum Image Dataset, presents collection of images specifically curated to support machine learning research in plant disease detection and classification.

Focusing on Pisum sativum (pea)—a widely cultivated legume and critical food source—the dataset captures a broad range of visual symptoms associated with fungal, viral, and pest-induced diseases. It contains 12,096 labeled images, distributed across 10 distinct classes:

Anthracnose (645 images)

Ascochyta blight (3,839 images)

Botrytis blight (1,457 images)

Downy mildew (315 images)

Fusarium wilt (640 images)

Pea leaf roll virus (40 images)

Pod borer damage (891 images)

Powdery mildew (529 images)

Stem rot (41 images)

Healthy samples (3,699 images)

Captured under diverse conditions—varying in lighting, background, and severity—the dataset provides a challenging and representative benchmark for developing and evaluating computer vision algorithms.

This resource enables both binary classification (healthy vs. diseased) and multiclass classification tasks. It is well-suited for experimentation with convolutional neural networks (CNNs), transfer learning, and data augmentation strategies. Researchers can leverage the dataset to improve the robustness and generalization of AI models for real-time disease detection.

The Pisum sativum Image Dataset offers a valuable foundation for research in precision agriculture, agricultural informatics, and AI-powered crop monitoring. It is intended to support both academic researchers and industry professionals working toward scalable and accurate solutions for plant health management.
DOI: 10.17632/nnv3k3m94k.1
title: Pisum sativum Image Dataset: Healthy and Disease-Affected Cases

description: Early and accurate detection of plant diseases is vital for sustainable agriculture and optimizing crop yields. This dataset, titled the Pisum sativum Image Dataset, presents collection of images specifically curated to support machine learning research in plant disease detection and classification.

Focusing on Pisum sativum (pea)—a widely cultivated legume and critical food source—the dataset captures a broad range of visual symptoms associated with fungal, viral, and pest-induced diseases. It contains 12,096 labeled images, distributed across 10 distinct classes:

Anthracnose (645 images)

Ascochyta blight (3,839 images)

Botrytis blight (1,457 images)

Downy mildew (315 images)

Fusarium wilt (640 images)

Pea leaf roll virus (40 images)

Pod borer damage (891 images)

Powdery mildew (529 images)

Stem rot (41 images)

Healthy samples (3,699 images)

Captured under diverse conditions—varying in lighting, background, and severity—the dataset provides a challenging and representative benchmark for developing and evaluating computer vision algorithms.

This resource enables both binary classification (healthy vs. diseased) and multiclass classification tasks. It is well-suited for experimentation with convolutional neural networks (CNNs), transfer learning, and data augmentation strategies. Researchers can leverage the dataset to improve the robustness and generalization of AI models for real-time disease detection.

The Pisum sativum Image Dataset offers a valuable foundation for research in precision agriculture, agricultural informatics, and AI-powered crop monitoring. It is intended to support both academic researchers and industry professionals working toward scalable and accurate solutions for plant health management.
DOI: 10.5281/zenodo.15646129
title: SoliDAIR defect detection dataset v1 for UC CIE

description: SoliDAIR defect detection dataset v1 for UC CIE.

Image data of surface and thread defects in aluminum high-pressure die casting (HPDC) automotive components of CIE Automotive.
DOI: 10.5281/zenodo.15646178
title: Responses of Nutrient and Chlorophyll_a to the Zhemin Coastal Water and Hanjiang River Plume off the eastern Guangdong Coast in Winter
DOI: 10.17632/g9skzg7d3p
title: SUPPLEMENTARY DATA FOR COVID-19 FRAMING DIRECTIONS IN NIGERIAN ONLINE NEWSPAPERS

description: Ethical approval from CHREC, Dates of newspaper editions and Coding guide for COVID-19 FRAMING DIRECTIONS IN NIGERIAN ONLINE NEWSPAPERS.
DOI: 10.17632/g9skzg7d3p.1
title: SUPPLEMENTARY DATA FOR COVID-19 FRAMING DIRECTIONS IN NIGERIAN ONLINE NEWSPAPERS

description: Ethical approval from CHREC, Dates of newspaper editions and Coding guide for COVID-19 FRAMING DIRECTIONS IN NIGERIAN ONLINE NEWSPAPERS.
DOI: 10.5281/zenodo.15646230
title: Asanyukta Kathak Mudra

description: Description:

The Asanyukta Kathak Mudra Gestures Dataset was meticulously collected to train a model on recognizing Kathak Mudras, aiding my little sister in perfecting her dance form by identifying errors and other challenges. This dataset includes images of various Asamyukta Hasta Mudras from Kathak, a classical Indian dance form. It is designed to support the development of AI models that can recognize and analyze these intricate gestures, serving as a valuable resource for both cultural preservation and technological advancement.

Download Dataset



Applications and Use Cases

Gesture Recognition: Train machine learning models to accurately recognize and classify different Kathak mudras.

Cultural Preservation: Utilize AI technology to preserve and document traditional dance forms, ensuring they remain accessible for future generations.

Educational Tools: Develop innovative tools to assist Kathak practitioners in learning, practicing, and perfecting their gestures with real-time feedback.

Art and Technology Integration: Explore and enhance the intersection of classical arts and modern technology, fostering new forms of artistic expression.


How to Use the Dataset



Download the Dataset: Access and download the dataset from Kaggle or the provided source.

Set Up Your Environment: Ensure all necessary dependencies and software are installed in your development environment.

Train Your Model: Utilize the training images from the dataset to train your machine learning model. Use validation images to fine-tune hyperparameters and prevent overfitting.

Test Your Model: Evaluate your model’s performance using the test images to ensure accuracy and reliability in recognizing Kathak mudras.


This dataset is sourced from Kaggle.
DOI: 10.5281/zenodo.15646231
title: Asanyukta Kathak Mudra

description: Description:

The Asanyukta Kathak Mudra Gestures Dataset was meticulously collected to train a model on recognizing Kathak Mudras, aiding my little sister in perfecting her dance form by identifying errors and other challenges. This dataset includes images of various Asamyukta Hasta Mudras from Kathak, a classical Indian dance form. It is designed to support the development of AI models that can recognize and analyze these intricate gestures, serving as a valuable resource for both cultural preservation and technological advancement.

Download Dataset



Applications and Use Cases

Gesture Recognition: Train machine learning models to accurately recognize and classify different Kathak mudras.

Cultural Preservation: Utilize AI technology to preserve and document traditional dance forms, ensuring they remain accessible for future generations.

Educational Tools: Develop innovative tools to assist Kathak practitioners in learning, practicing, and perfecting their gestures with real-time feedback.

Art and Technology Integration: Explore and enhance the intersection of classical arts and modern technology, fostering new forms of artistic expression.


How to Use the Dataset



Download the Dataset: Access and download the dataset from Kaggle or the provided source.

Set Up Your Environment: Ensure all necessary dependencies and software are installed in your development environment.

Train Your Model: Utilize the training images from the dataset to train your machine learning model. Use validation images to fine-tune hyperparameters and prevent overfitting.

Test Your Model: Evaluate your model’s performance using the test images to ensure accuracy and reliability in recognizing Kathak mudras.


This dataset is sourced from Kaggle.
DOI: 10.17632/gb8jp62vm5.1
title: 86-Y-Metereological_data

description: The datasets are composed mainly of 4 variables: precipitation, evaporation, maximum and minimum temperature. The data generated by the meteorological stations were stored in txt files by CONAGUA. Therefore, these files were preprocessed and transformed into csv format, the final csv files are compatible with several programming languages, such as Python, R, MATLAB and SCALA
These long-term meteorological datasets can be used for water balances, flood and drought simulations, identifying climate changes and meteorological anthropogenic influences in the study area
A mean of 21,200 records per meteorological station were processed and prepared over approximately for the 1933 – 2019 period. The data is available in raw, daily ,monthly and annual resolutions.
Datasets contain relevant information related to meteorological stations, such as name, identification, quantity of rows, quantity of null values, latitude, longitude and range date. Some stations are located in high mountain relief, valley and coastal zones and other stations located in urbanized areas.
DOI: 10.17632/whgy3tjw7k
title: Saudi Female Engineering Students

description: Saudi Female Engineering Students
DOI: 10.17632/whgy3tjw7k.1
title: Saudi Female Engineering Students

description: Saudi Female Engineering Students
DOI: 10.17632/kwr7xfg4kp
title: Narrative Brief Biography of Dejazmach Woldesemait Gebrewold and his Contributions to Good Governance and Development of Wolaita, South Ethiopia, 1963-1970

description: Based on the aim and objectives of the study, this work adopted a qualitative research method. Qualitative research method best fits for exploring the life of individual, social phenomena, events and get new knowledge. The study adopted a biographical research design.
Levinson (1986) said the following about the biographical research፡ 
The biographical method is the only one that enables us to obtain a complex picture of the life structure at a given time and to delineate the evolution of the life structure over a span of years. It is well suited for gaining a more concrete sense of the individual life course, for generating new concepts, and in time, for developing new variables, measures and hypotheses. 
Biographical researches of individual life histories fall into the category of retrospective longitudinal research techniques (Solga, 2001), which are employed to describe patterns of change and the quality (course and scale) of those changes over time (Menard, 2002). The design helps to assess the changes of Wolaita introduced by the governor. The data employed in this paper were collected when the writer was at fieldwork in 2016 and 2017. The data gathering techniques employed were detail interview of key informants and assessment of secondary sources and archival documents.
Denzin (2009) categorizes two types of life histories: complete and topical. The complete life history grasps the entire life of the subject; the subject’s past remodeled in all of its complexity, grounded on the personal story of the individual. The topical life history focuses on specific thematic periods in someone’s life; for example, the period of his education. The present work utilized the topical life history type that emphasizes on the work life history of the person particularly when he was governor of Wolaita in the 1960s. The study carried out 9 face-to-face semi-structured in-depth interviews with older citizens whose all ages are between 65 and 92 years old to uncover the life history of the subject and his contributions in line of good governance and development.
The number of informants is believed to be appropriate and limited to nine persons only because the persons are selected purposely and they are key informants who lived and worked in Wolaita in the study time. They had worked and contacted with the governor in different times and had sufficient knowledge of the subject under study. The study is not only depended on the interview but also on analysis of written texts and archival sources, for example autobiographical writings, letters and publications. Crosscheck of data accuracy has been made to prove the credibility and authenticity of the sources. The primary sources analyzed were available from the hands of individuals and the library of Addis Ababa University. The available data are analyzed by using descriptive and historical content narrative analysis methods. This data contains the photo of of the Wolaita man, map and letter.
DOI: 10.17632/kwr7xfg4kp.1
title: Narrative Brief Biography of Dejazmach Woldesemait Gebrewold and his Contributions to Good Governance and Development of Wolaita, South Ethiopia, 1963-1970

description: Based on the aim and objectives of the study, this work adopted a qualitative research method. Qualitative research method best fits for exploring the life of individual, social phenomena, events and get new knowledge. The study adopted a biographical research design.
Levinson (1986) said the following about the biographical research፡ 
The biographical method is the only one that enables us to obtain a complex picture of the life structure at a given time and to delineate the evolution of the life structure over a span of years. It is well suited for gaining a more concrete sense of the individual life course, for generating new concepts, and in time, for developing new variables, measures and hypotheses. 
Biographical researches of individual life histories fall into the category of retrospective longitudinal research techniques (Solga, 2001), which are employed to describe patterns of change and the quality (course and scale) of those changes over time (Menard, 2002). The design helps to assess the changes of Wolaita introduced by the governor. The data employed in this paper were collected when the writer was at fieldwork in 2016 and 2017. The data gathering techniques employed were detail interview of key informants and assessment of secondary sources and archival documents.
Denzin (2009) categorizes two types of life histories: complete and topical. The complete life history grasps the entire life of the subject; the subject’s past remodeled in all of its complexity, grounded on the personal story of the individual. The topical life history focuses on specific thematic periods in someone’s life; for example, the period of his education. The present work utilized the topical life history type that emphasizes on the work life history of the person particularly when he was governor of Wolaita in the 1960s. The study carried out 9 face-to-face semi-structured in-depth interviews with older citizens whose all ages are between 65 and 92 years old to uncover the life history of the subject and his contributions in line of good governance and development.
The number of informants is believed to be appropriate and limited to nine persons only because the persons are selected purposely and they are key informants who lived and worked in Wolaita in the study time. They had worked and contacted with the governor in different times and had sufficient knowledge of the subject under study. The study is not only depended on the interview but also on analysis of written texts and archival sources, for example autobiographical writings, letters and publications. Crosscheck of data accuracy has been made to prove the credibility and authenticity of the sources. The primary sources analyzed were available from the hands of individuals and the library of Addis Ababa University. The available data are analyzed by using descriptive and historical content narrative analysis methods. This data contains the photo of of the Wolaita man, map and letter.
DOI: 10.5281/zenodo.15584835
title: Data for: Transient infrared nanoscopy resolves the millisecond photoswitching dynamics of single lipid vesicles in water
DOI: 10.5281/zenodo.15584834
title: Data for: Transient infrared nanoscopy resolves the millisecond photoswitching dynamics of single lipid vesicles in water
DOI: 10.17632/bpwj3tjrph.1
title: Supplementary Information

description: Supplementary Information
DOI: 10.17632/9mcp5pbtbr.1
title: BIOSE-Non-labelled MRI Dataset for Unsupervised Learning

description: # BIOSE:Non-labelled MRI Dataset for Unsupervised Learning

## Overview

This dataset contains anonymized multimodal brain MRI scans from 40 subjects, formatted according to the [BIDS (Brain Imaging Data Structure) 1.8.0](https://bids.neuroimaging.io/) standard. It is designed to support **unsupervised and self-supervised learning research** in medical imaging.

---

## Dataset Summary

* **Subjects**: 40 (`sub-00` to `sub-39`)
* **Sessions per Subject**: 1 (`ses-01`)
* **Total Files**: 497
* **Total Size**: 817.65 MB
* **Modalities**:

 * Anatomical MRI: T1w, T2w, FLAIR, T2star
 * Diffusion MRI (DWI): Includes `.nii.gz`, `.bval`, and `.bvec` files

---

## Directory Structure

```
dataset_root/
├── dataset_description.json
├── participants.tsv
├── README.md
├── sub-00/
│ └── ses-01/
│ ├── anat/
│ │ ├── sub-00_ses-01_T1w.nii.gz
│ │ ├── sub-00_ses-01_T2w.nii.gz
│ │ ├── sub-00_ses-01_FLAIR.nii.gz
│ │ └── sub-00_ses-01_T2star.nii.gz
│ └── dwi/
│ ├── sub-00_ses-01_dwi.nii.gz
│ ├── sub-00_ses-01_dwi.bval
│ └── sub-00_ses-01_dwi.bvec
├── ...
└── sub-39/
```

---

## Files in the Root Directory

* `dataset_description.json`: Metadata describing the dataset name, authors, license, and funding.
* `participants.tsv`: Table listing participants (subjects) and any relevant metadata (anonymized).
* `README.md`: This file.

---

## Intended Use

This dataset is best suited for:

* **Unsupervised MRI feature learning**
* **Contrastive or generative modeling**
* **Modality synthesis and harmonization**
* **Diffusion MRI pre-processing or denoising**

No diagnostic labels or clinical metadata are provided.

---

## License

**CC0 (Creative Commons Zero)** – No rights reserved. Free to use for research, education, and commercial purposes.

---

## Acknowledgements

* MRI scans sourced from **Islami Bank Central Hospital**
* Dataset organized and anonymized by researchers from **BRAC University** and the **Biomedical Science and Engineering (BIOSE) Research Group**

---

## Citation

When using this dataset, please cite:

Noor Rahman, Tahmid ; Paul, Tonmoy; Zarin Tasnim, Tahiat; Hasan Shawon, Mehedi ; Bin Reza, Rifat; Hasan, Fardin; Sumaiya, Akter; Jobayer, Md (2025), “BIOSE-Non-labelled MRI Dataset for Unsupervised Learning”, Mendeley Data, V1, doi: 10.17632/9mcp5pbtbr.1

---
DOI: 10.17632/9n53tbjcr8
title: Disparities in Acral Lentiginous Melanoma: Racial Factors Beyond Delayed Diagnosis (Supplemental Material)

description: Supplementary material for "Disparities in Acral Lentiginous Melanoma: Racial Factors Beyond Delayed Diagnosis".
DOI: 10.17632/9n53tbjcr8.2
title: Disparities in Acral Lentiginous Melanoma: Racial Factors Beyond Delayed Diagnosis (Supplemental Material)

description: Supplementary material for "Disparities in Acral Lentiginous Melanoma: Racial Factors Beyond Delayed Diagnosis".
DOI: 10.17632/zhm2j4n8n8
title: Antibiotic Susceptibility Profiles of Post-Chlorination Bacterial Isolates from Urban Wastewater Treatment Plants in Gujarat, India

description: This dataset offers a thorough examination of the patterns of antibiotic susceptibility in bacterial isolates that were found in the chlorinated wastewater of five sewage treatment plants (STPs) in Gujarat, India—two in Vadodara and three in Anand. In total, 19 bacterial isolates have been obtained after in situ chlorination that includes 11 Gram-positive and 8 Gram-negative strains. Antimicrobial susceptibility testing was performed on these isolates using the Kirby-Bauer disk diffusion technique, and the findings were interpreted according to the Clinical and Laboratory Standards Institute (CLSI) M100-S25 Guidelines. This study's main goal was to determine the distribution and prevalence of antimicrobial resistance (AMR) in bacteria that survive final-stage disinfection in municipal solid waste treatment plants. Thirty-one antibiotics were examined: Penicillin-G, Oxacillin, Ampicillin, Cefotaxime, Vancomycin, Teicoplanin, Tetracycline, Gentamicin, Erythromycin, Azithromycin, Chloramphenicol, Clindamycin, Norfloxacin, Ofloxacin, and Co-Trimazine are among the 16 antibiotics that target Gram-positive bacteria, while 15 antibiotics that target Gram-negative bacteria include Piperacillin/Tazobactam, Cefoperazone/Sulbactam, Carbenicillin, Tetracycline, Doxycycline Hydrochloride, Amikacin, Gentamicin, Streptomycin, Kanamycin, Ciprofloxacin, Ofloxacin, Gatifloxacin, Co-Trimazine, and Nitrofurantoin. In both bacterial groups, the effects of four broad-spectrum antibiotics—Tetracycline, Gentamicin, Ofloxacin, and Co-Trimazine—were examined. Based on CLSI breakpoints, each zone of inhibition (ZOI) was classified as susceptible, intermediate, or resistant and measured in millimetres. With color-coded annotations—green for susceptible, yellow for intermediate, and red for resistant—the data are displayed in a comprehensive categorization matrix. The widespread nature of resistance across bacterial species and antibiotic classes may be easily understood using this approach. Two sets of comparative graphical analyses are included in the dataset to improve interpretability. Potential multi-drug resistance is highlighted in the first set, which shows the antibiotic resistance profile for each distinct isolate. In order to determine which antibiotics are the most and least effective in the post-chlorination setting, the second set evaluates the effectiveness of each antibiotic across all isolates. These visual representations provide helpful insights on the effectiveness of disinfection processes and microbial tolerance to chlorine stress. The area of environmental AMR surveillance is growing, and this dataset adds to it, especially for treated wastewater systems, which are frequently not included in national monitoring programs. Integrated AMR monitoring inside STPs is necessary because of the documented resistance in post-treatment isolates, which highlights the possible risk of releasing resistant bacteria into natural environments.