cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/44rrmmpr33.1
title: Good and Bad classification of Cucumber ( Cucumis Sativus)

description: Good and Bad Classification of Cucumber:-

1. Introduction

This section introduces cucumber quality assessment and its importance in agriculture, food processing, and market distribution. It explains how manual inspection is time-consuming and subjective, and highlights the need for an automated image-based classification system to distinguish between good and bad cucumbers.

2. Objective of the Study

The main objective of this research is to develop a system that classifies cucumbers into good and bad categories using digital images. The study aims to improve accuracy and efficiency in quality evaluation by analyzing visual characteristics.

3. Dataset Description

A dataset of 1000 cucumber images was used in this research, consisting of 500 good-quality images and 500 bad-quality images.

Good cucumber images show fresh cucumbers with uniform green color, firm texture, smooth surface, and no visible defects.

Bad cucumber images include cucumbers with discoloration, deformation, surface damage, softness, mold, or signs of spoilage.
The balanced dataset helps reduce bias during model training and evaluation.

4. Image Preprocessing

All images were resized to a uniform resolution to maintain consistency. Noise removal and normalization techniques were applied to enhance image quality. These preprocessing steps help improve feature extraction and classification accuracy.

5. Feature Extraction

Important visual features such as color distribution, texture patterns, and shape characteristics were extracted from the images. These features play a key role in distinguishing good cucumbers from bad ones.

6. Classification Methodology

A classification model was trained using the extracted features and labeled images. The model learns quality-related patterns from the training data and predicts whether a cucumber image belongs to the good or bad class.

7. Performance Evaluation

The classification system was evaluated using standard performance metrics such as accuracy, precision, recall, and F1-score. These metrics help measure the effectiveness of the proposed method.

8. Results and Discussion

This section analyzes the classification results and discusses how well the system identifies quality differences between good and bad cucumbers. Observations related to misclassification and model performance are also presented.

9. Conclusion

The study concludes that image-based classification can effectively distinguish between good and bad cucumbers. The proposed approach reduces manual inspection effort and supports automated quality assessment.

10. Future Scope

Future work may include increasing the dataset size, applying deep learning techniques, or extending the approach to classify other vegetables and fruits.
DOI: 10.17632/nc8k63t7mp
title: HistoSet-5×14: A Collection of Balanced Multi-Organ Histopathology Datasets

description: HistoSet-5×14 is a secondary, derived histopathological image collection created by aggregating, standardizing, and rebalancing images from multiple publicly available and peer reviewed histopathology datasets. The collection covers five organs, namely breast, colon, lung, oral cavity, and ovary, and consists of fourteen cancerous and non cancerous tissue classes. The dataset is intended to support multi class and multi organ machine learning research in computational pathology. The images included in HistoSet-5×14 originate from four established sources. These include the Lung and Colon Cancer Histopathological Image Dataset (LC25000) introduced by Borkowski et al. (2019), the Breast Cancer Histopathological Image Classification dataset by Spanhol et al. (2015), the Oral Cancer Histopathological Imaging Database by Rahman et al. (2020), and the Ovarian Cancer Histopathology dataset proposed by Kasture et al. (2021). All source datasets consist of Hematoxylin and Eosin stained histopathological images that are de identified and publicly available for research use.

Since the original datasets exhibit substantial class imbalance and heterogeneous sample sizes, HistoSet-5×14 applies a standardized preprocessing pipeline involving controlled data augmentation for under-represented classes and down-sampling of over-represented classes. Each class was normalized to 2,000 images, yielding a total of 28,000 images. Augmentation was performed conservatively to preserve diagnostically relevant morphological patterns while improving class balance and model robustness.

Source Dataset References:
1. Borkowski, A.A., et al. (2019). Lung and colon cancer histopathological image dataset (LC25000). arXiv:1912.12142.
2. Spanhol, F.A., et al. (2015). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462.
3. Rahman, T.Y., et al. (2020). Histopathological imaging database for oral cancer analysis. Data in Brief, 29, 105114.
4. Kasture, K.R., et al. (2021). A new deep learning method for automatic ovarian cancer prediction & subtype classification. Turkish Journal of Computer and Mathematics Education, 12(12), 1233–1242.
DOI: 10.24414/rob-euref-focs00rou
title: GNSS data of FOCS00ROU, Focsani, Romania

description: Permanent GNSS station dataset as RINEX files
DOI: 10.17632/nc8k63t7mp.1
title: HistoSet-5×14: A Collection of Balanced Multi-Organ Histopathology Datasets

description: HistoSet-5×14 is a secondary, derived histopathological image collection created by aggregating, standardizing, and rebalancing images from multiple publicly available and peer reviewed histopathology datasets. The collection covers five organs, namely breast, colon, lung, oral cavity, and ovary, and consists of fourteen cancerous and non cancerous tissue classes. The dataset is intended to support multi class and multi organ machine learning research in computational pathology. The images included in HistoSet-5×14 originate from four established sources. These include the Lung and Colon Cancer Histopathological Image Dataset (LC25000) introduced by Borkowski et al. (2019), the Breast Cancer Histopathological Image Classification dataset by Spanhol et al. (2015), the Oral Cancer Histopathological Imaging Database by Rahman et al. (2020), and the Ovarian Cancer Histopathology dataset proposed by Kasture et al. (2021). All source datasets consist of Hematoxylin and Eosin stained histopathological images that are de identified and publicly available for research use.

Since the original datasets exhibit substantial class imbalance and heterogeneous sample sizes, HistoSet-5×14 applies a standardized preprocessing pipeline involving controlled data augmentation for under-represented classes and down-sampling of over-represented classes. Each class was normalized to 2,000 images, yielding a total of 28,000 images. Augmentation was performed conservatively to preserve diagnostically relevant morphological patterns while improving class balance and model robustness.

Source Dataset References:
1. Borkowski, A.A., et al. (2019). Lung and colon cancer histopathological image dataset (LC25000). arXiv:1912.12142.
2. Spanhol, F.A., et al. (2015). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462.
3. Rahman, T.Y., et al. (2020). Histopathological imaging database for oral cancer analysis. Data in Brief, 29, 105114.
4. Kasture, K.R., et al. (2021). A new deep learning method for automatic ovarian cancer prediction & subtype classification. Turkish Journal of Computer and Mathematics Education, 12(12), 1233–1242.
DOI: 10.5281/zenodo.17937031
title: BSgenome and Txdb for 10x Genomics pre-built references

description: The BSgenome R/Bioconductor packages and TxDb objects in this repository are created from the reference genomes and annotation files used by the 10x Genomics pre-built references.

Using these data is ideal when you want to perform additional analyse on data that has been pre-processed by Cell Ranger and Cell Ranger ARC, with a specific version of the pre-built reference, e.g. Multiome (ATAC + Gene Expression) dataset.
DOI: 10.5281/zenodo.17937032
title: BSgenome and Txdb for 10x Genomics pre-built references

description: The BSgenome R/Bioconductor packages and TxDb objects in this repository are created from the reference genomes and annotation files used by the 10x Genomics pre-built references.

Using these data is ideal when you want to perform additional analyse on data that has been pre-processed by Cell Ranger and Cell Ranger ARC, with a specific version of the pre-built reference, e.g. Multiome (ATAC + Gene Expression) dataset.
DOI: 10.17632/k82g9whv3v
title: Mechanism of impurity (Al, Si, P, W, Mo, and Cr) migration in the process of producing high-purity NH4VO3 through a novel cation exchange approach

description: The impurity migration behavior and competitive reaction mechanism of preparing high-purity NH4VO3 by a new cation exchange method were studied, and an efficient and environmentally friendly approach for the production of NH4VO3 was established. This dataset includes the single-factor experimental results of the calcification stage and the ammonification stage, as well as the theoretical analysis of the priority of calcification reactions, the characterization of calcification and ammonification products, clarifies the migration pathways and competitive transformation of key impurities in the purification process of the stepwise cation exchange method, and reveals the stepwise behavior of impurity types during the precipitation and dissolution processes.
DOI: 10.17632/k82g9whv3v.1
title: Mechanism of impurity (Al, Si, P, W, Mo, and Cr) migration in the process of producing high-purity NH4VO3 through a novel cation exchange approach

description: The impurity migration behavior and competitive reaction mechanism of preparing high-purity NH4VO3 by a new cation exchange method were studied, and an efficient and environmentally friendly approach for the production of NH4VO3 was established. This dataset includes the single-factor experimental results of the calcification stage and the ammonification stage, as well as the theoretical analysis of the priority of calcification reactions, the characterization of calcification and ammonification products, clarifies the migration pathways and competitive transformation of key impurities in the purification process of the stepwise cation exchange method, and reveals the stepwise behavior of impurity types during the precipitation and dissolution processes.
DOI: 10.17632/rf5m97cmm3
title: Thermal and RGB Images of Chronic Venous Insufficiency –CEAP : TRICVI–C

description: This database contains thermal and RGB images of chronic venous insufficiency of the lower extremities. Each volunteer was diagnosed by an angiologist based on the CEAP standard. The images were acquired according to a standardized protocol to ensure the consistency and reproducibility of the study.
DOI: 10.5281/zenodo.17939999
title: Emotion regulation and repeated exposure to stress - data set
DOI: 10.5281/zenodo.17940000
title: Emotion regulation and repeated exposure to stress - data set
DOI: 10.17632/rf5m97cmm3.1
title: Thermal and RGB Images of Chronic Venous Insufficiency –CEAP : TRICVI–C

description: This database contains thermal and RGB images of chronic venous insufficiency of the lower extremities. Each volunteer was diagnosed by an angiologist based on the CEAP standard. The images were acquired according to a standardized protocol to ensure the consistency and reproducibility of the study.
DOI: 10.15468/dl.vcq89g
title: Occurrence Download

description: A dataset containing 128 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acmaeoderella cyanipennis perroti"
 ]
}

 The dataset includes 128 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053718-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/d42n3cp86p
title: SkySeaLand Satellite Object Detection Dataset

description: This dataset was created to support a research idea that transportation-related objects in satellite imagery can be detected with steady accuracy when they are labeled with clear and consistent annotations. It focuses on four object classes: airplane, boat, car, and ship, and includes scenes from airports, highways, harbors, marinas, and coastal regions. These locations were selected to cover different backgrounds, object scales, and environmental conditions, which helps in studying model performance in realistic satellite settings.

Images were collected using Google Earth Pro under fair use and academic research guidelines. Candidate regions were explored manually and exported in high resolution through the built-in tools of the software. After collection, each image was reviewed for clarity, relevance, and presence of target objects. Files with heavy noise, strong cloud cover, or duplicate viewpoints were removed. When needed, basic preparation such as cropping and resizing was applied to keep the focus on relevant areas while maintaining visual quality.

Annotation was carried out using CVAT and Roboflow. Each object instance was marked with a bounding box and assigned one of the four class labels. A separate verification pass was performed to maintain consistent box placement and correct class assignment across the dataset. For this release, all final annotations are stored in a single COCO format JSON file that follows the standard object detection structure. The images are organized into separate folders for training, validation, and testing. A Roboflow project link is provided so that users can view the dataset online, apply their own preprocessing pipeline, and export the same annotations into formats such as YOLO or Pascal VOC if required.

The dataset can be interpreted by loading the COCO JSON file, reading the category identifiers, and mapping them to airplane, boat, car, and ship. Each annotation entry provides bounding box coordinates and class information that can be used directly with common computer vision libraries. Researchers can use this dataset to test model generalization across land and sea scenes, evaluate multi-class detection performance, study small object behavior, compare detection architectures, and explore transfer learning strategies in aerial imagery.
DOI: 10.17632/d42n3cp86p.3
title: SkySeaLand Satellite Object Detection Dataset

description: This dataset was created to support a research idea that transportation-related objects in satellite imagery can be detected with steady accuracy when they are labeled with clear and consistent annotations. It focuses on four object classes: airplane, boat, car, and ship, and includes scenes from airports, highways, harbors, marinas, and coastal regions. These locations were selected to cover different backgrounds, object scales, and environmental conditions, which helps in studying model performance in realistic satellite settings.

Images were collected using Google Earth Pro under fair use and academic research guidelines. Candidate regions were explored manually and exported in high resolution through the built-in tools of the software. After collection, each image was reviewed for clarity, relevance, and presence of target objects. Files with heavy noise, strong cloud cover, or duplicate viewpoints were removed. When needed, basic preparation such as cropping and resizing was applied to keep the focus on relevant areas while maintaining visual quality.

Annotation was carried out using CVAT and Roboflow. Each object instance was marked with a bounding box and assigned one of the four class labels. A separate verification pass was performed to maintain consistent box placement and correct class assignment across the dataset. For this release, all final annotations are stored in a single COCO format JSON file that follows the standard object detection structure. The images are organized into separate folders for training, validation, and testing. A Roboflow project link is provided so that users can view the dataset online, apply their own preprocessing pipeline, and export the same annotations into formats such as YOLO or Pascal VOC if required.

The dataset can be interpreted by loading the COCO JSON file, reading the category identifiers, and mapping them to airplane, boat, car, and ship. Each annotation entry provides bounding box coordinates and class information that can be used directly with common computer vision libraries. Researchers can use this dataset to test model generalization across land and sea scenes, evaluate multi-class detection performance, study small object behavior, compare detection architectures, and explore transfer learning strategies in aerial imagery.
DOI: 10.17632/xc9tjwkd3s
title: Associations of Skin Cancer, Dermatitis, and Risk Factors for Skin Conditions with Epigenetic Aging Biomarkers Among a Representative Sample of U.S. Adults: NHANES 1999-2002 - Supplementary Material

description: This document contains supplemental information regarding the methodology and results of this study, including more details about statistical analysis and regression results.
DOI: 10.17632/xc9tjwkd3s.2
title: Associations of Skin Cancer, Dermatitis, and Risk Factors for Skin Conditions with Epigenetic Aging Biomarkers Among a Representative Sample of U.S. Adults: NHANES 1999-2002 - Supplementary Material

description: This document contains supplemental information regarding the methodology and results of this study, including more details about statistical analysis and regression results.
DOI: 10.17632/k6hts2ktxw
title: Chattogram sent: A Multilingual Sentiment Dataset for Chattogram, Bengali , and English

description: The Chattogram dialect (Chittangga), widely spoken in southeastern Bangladesh, is primarily an oral language with no standardized writing system. Despite its large speaker population, the dialect remains underrepresented in computational linguistics due to the scarcity of high-quality, manually curated digital resources.

This dataset introduces a fully manual, native-curated multilingual sentiment corpus developed entirely by researchers who are native speakers of the Chattogram dialect. It consists of 4,452 parallel sentences aligned across Chattogram, Standard Bangla, and English. The data were collected from authentic sources, including social media posts, regional drama scripts, and everyday conversations, ensuring natural and context-rich language usage.

The Chattogram dialect is predominantly spoken in Chattogram city, Cox’s Bazar, and the coastal regions of the Chittagong Hill Tracts, as well as nearby districts of southeastern Bangladesh. Given the oral nature of the dialect, all Chattogram sentences were phonetically transcribed into Bengali script. The dataset follows a translation-first pipeline: each Chattogram sentence was translated into Standard Bangla and then English by the same native speakers to maintain semantic fidelity and cross-lingual alignment. Sentiment annotation was performed after multilingual alignment, with each sentence categorized as Neutral, Negative, or Positive (Neutral: 1,969; Negative: 1,467; Positive: 1,016).

The dataset represents the first high-quality benchmark for sentiment analysis in the Chattogram dialect, enabling researchers to develop low-resource NLP models, dialectal sentiment classifiers, and cross-lingual transformer-based systems. Its native-driven design ensures linguistic authenticity, cultural accuracy, and contextual relevance, providing a valuable resource for the computational study of underrepresented languages.

By combining manual transcription, expert multilingual translation, and careful sentiment annotation, this corpus supports both academic research and practical applications in natural language processing, multilingual AI systems, and digital preservation of oral language traditions.
DOI: 10.17632/k6hts2ktxw.1
title: Chattogram sent: A Multilingual Sentiment Dataset for Chattogram, Bengali , and English

description: The Chattogram dialect (Chittangga), widely spoken in southeastern Bangladesh, is primarily an oral language with no standardized writing system. Despite its large speaker population, the dialect remains underrepresented in computational linguistics due to the scarcity of high-quality, manually curated digital resources.

This dataset introduces a fully manual, native-curated multilingual sentiment corpus developed entirely by researchers who are native speakers of the Chattogram dialect. It consists of 4,452 parallel sentences aligned across Chattogram, Standard Bangla, and English. The data were collected from authentic sources, including social media posts, regional drama scripts, and everyday conversations, ensuring natural and context-rich language usage.

The Chattogram dialect is predominantly spoken in Chattogram city, Cox’s Bazar, and the coastal regions of the Chittagong Hill Tracts, as well as nearby districts of southeastern Bangladesh. Given the oral nature of the dialect, all Chattogram sentences were phonetically transcribed into Bengali script. The dataset follows a translation-first pipeline: each Chattogram sentence was translated into Standard Bangla and then English by the same native speakers to maintain semantic fidelity and cross-lingual alignment. Sentiment annotation was performed after multilingual alignment, with each sentence categorized as Neutral, Negative, or Positive (Neutral: 1,969; Negative: 1,467; Positive: 1,016).

The dataset represents the first high-quality benchmark for sentiment analysis in the Chattogram dialect, enabling researchers to develop low-resource NLP models, dialectal sentiment classifiers, and cross-lingual transformer-based systems. Its native-driven design ensures linguistic authenticity, cultural accuracy, and contextual relevance, providing a valuable resource for the computational study of underrepresented languages.

By combining manual transcription, expert multilingual translation, and careful sentiment annotation, this corpus supports both academic research and practical applications in natural language processing, multilingual AI systems, and digital preservation of oral language traditions.
DOI: 10.17632/v2kk9kd6gn.1
title: Add-on Bach flower remedies to individualized homeopathic medicines in mild to moderate depression: An open, randomized factorial pilot trial

description: This pilot randomized controlled trial assessed Bach flower remedies (BFR), individualized homeopathic medicines (IHMs), and their combination against placebo in 100 adults with mild-to-moderate depression over three months, using Hamilton Depression Rating Scale (HDRS) as primary outcome and Beck Depression Inventory (BDI) as secondary, revealing significant symptom reductions in active arms (HDRS mean differences vs. placebo: IHMs -7.12, combined -6.54, BFR -5.14; all p&lt;0.001) with 88-92% responder rates (NNT 1.1), rapid onset (40% in IHMs/combined by month 1), 100% Bayesian superiority probability, and 36.8% IHMs effect mediated via somatic relief, confirmed robust across subgroups via GEE, mixed models, and sensitivity analyses, with only seven mild transient adverse events and no serious issues, supporting these safe complementary therapies as promising adjuncts warranting larger blinded confirmatory trials.
DOI: 10.15468/dl.m8f3ur
title: Occurrence Download

description: A dataset containing 1087 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Gabon",
 "TaxonKey is Aucoumea klaineana"
 ]
}

 The dataset includes 1087 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0053719-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.