cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/5m4x8mv52d.1
title: Image Dataset of Multiple Plant Species Under Different Lighting Modes of Plant Growth Lamps

description: This dataset is developed for plant factory agriculture scenarios to support research related to the intelligent operation of such factories. As an efficient agricultural model, plant factories enhance crop production efficiency through vertical farming and precise environmental control, and can address global challenges like population growth and arable land scarcity. LEDs have become the mainstream light source in these factories due to their low power consumption and narrow spectral output; however, the red-blue light they consistently use causes significant color distortion in plant images. This reduces the accuracy of machine vision in tasks such as crop physiological monitoring, pest and disease detection, and other AI-driven work, hindering the intelligent upgrading of factories. Currently, most vision-based plant research relies on images captured under white light. Therefore, restoring color-distorted images from specific light sources (e.g., plant growth lamps) to a white-light equivalent state is a key requirement for the intelligent operation of plant factories—and this dataset is constructed to meet this need, serving the development and verification of plant image color correction algorithms.
DOI: 10.17632/p38cyb98tc.3
title: Single-cell and spatial transcriptomics reveal cellular dynamics in the regenerating heart

description: In neonatal mice, the heart possesses a robust regenerative capacity following injury, which is rapidly lost by postnatal day 8 and in adulthood. In this study, we performed an integrated analysis of the regenerating heart during the neonatal stage using single-cell and spatial transcriptomics. We developed and applied a novel algorithm, SDIMGO, to reconstruct spatial transcriptomic profiles at single-cell resolution. By delineating spatial domains, we identified functionally coordinated cellular ensembles, uncovering key spatial organizational features and cooperative mechanisms involved in cardiac regeneration.
DOI: 10.6084/m9.figshare.30024511
title: Pheno input "Unraveling the Genetic Basis of General Combining Ability in CIMMYT Elite Bread Wheat Germplasm: Implications for Breeding Strategies Optimization"

description: This is the phenotypic input used in the paper titled "Unraveling the Genetic Basis of General Combining Ability in CIMMYT Elite Bread Wheat Germplasm: Implications for Breeding Strategies Optimization"
DOI: 10.6084/m9.figshare.30024511.v1
title: Pheno input "Unraveling the Genetic Basis of General Combining Ability in CIMMYT Elite Bread Wheat Germplasm: Implications for Breeding Strategies Optimization"

description: This is the phenotypic input used in the paper titled "Unraveling the Genetic Basis of General Combining Ability in CIMMYT Elite Bread Wheat Germplasm: Implications for Breeding Strategies Optimization"
DOI: 10.17632/gyxpjf3pwc
title: LLM-powered voice assistants for music learning: Generational comparison of usability, device preference and learning outcomes

description: This dataset presents task records, brief interviews, and questionnaire survey data in the paper LLM-powered voice assistants for music learning: Generational comparison of usability, device preference and learning outcomes. User response records, questionnaire details, and questionnaire data are also shared in the dataset. These data are provided for readers' reference.
DOI: 10.17632/gyxpjf3pwc.3
title: LLM-powered voice assistants for music learning: Generational comparison of usability, device preference and learning outcomes

description: This dataset presents task records, brief interviews, and questionnaire survey data in the paper LLM-powered voice assistants for music learning: Generational comparison of usability, device preference and learning outcomes. User response records, questionnaire details, and questionnaire data are also shared in the dataset. These data are provided for readers' reference.
DOI: 10.17632/d2g7y8wftw
title: soil properties

description: This dataset includes soil physicochemical and microbial properties measured under different simulated acid rain (SAR) treatments.
DOI: 10.17632/d2g7y8wftw.1
title: soil properties

description: This dataset includes soil physicochemical and microbial properties measured under different simulated acid rain (SAR) treatments.
DOI: 10.17632/nfrgx2vbxt
title: GAI_Employee advocacy

description: GAI and Employee advocacy
DOI: 10.17632/nfrgx2vbxt.1
title: GAI_Employee advocacy

description: GAI and Employee advocacy
DOI: 10.17632/zzwgx2ztwy
title: Light-propelled Janus-like organic nanomotors for photo-mechanical cancer therapy

description: Reasearch Data of "Light-propelled Janus-like organic nanomotors for photo-mechanical cancer therapy"
DOI: 10.17632/zzwgx2ztwy.1
title: Light-propelled Janus-like organic nanomotors for photo-mechanical cancer therapy

description: Reasearch Data of "Light-propelled Janus-like organic nanomotors for photo-mechanical cancer therapy"
DOI: 10.15468/dl.5hf7hc
title: Occurrence Download

description: A dataset listing the 29152 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "OccurrenceStatus is Present",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset's 29152 records were derived from 400 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0016223-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/tfbnnzyrrf
title: Marking of corn seedling leaves

description: This dataset contains annotated images of corn seedling leaves, designed to support research in leaf image recognition, segmentation, and analysis for maize. It includes photographs of corn seedlings captured under various conditions, with each image accompanied by manual annotations indicating leaf regions or key points. The annotation data covers the boundary positions of leaves, numbering, and relevant growth status information, facilitating tasks such as leaf detection, morphological analysis, and disease identification. This dataset is suitable for training and testing machine learning and deep learning models, providing robust data support for intelligent agriculture applications related to monitoring and managing the growth of corn seedlings.
DOI: 10.17632/tfbnnzyrrf.1
title: Marking of corn seedling leaves

description: This dataset contains annotated images of corn seedling leaves, designed to support research in leaf image recognition, segmentation, and analysis for maize. It includes photographs of corn seedlings captured under various conditions, with each image accompanied by manual annotations indicating leaf regions or key points. The annotation data covers the boundary positions of leaves, numbering, and relevant growth status information, facilitating tasks such as leaf detection, morphological analysis, and disease identification. This dataset is suitable for training and testing machine learning and deep learning models, providing robust data support for intelligent agriculture applications related to monitoring and managing the growth of corn seedlings.
DOI: 10.17632/r5s89w8g7k.1
title: Whole genome of Lactobacillus helveticus MDC 9602

description: Data shared to Manuscript "Armenian functional food “Narine”: Characterization of fermenting bacteria Lactobacillus helveticus full genome sequences and potential functional food marker genes".
The aim of this study was to perform whole-genome sequencing of the famous "Narine" functional food starter culture, establish the precise classification of the strain, and utilize genomic and proteomic data to reveal the essential probiotic and prebiotic characteristics of the strain.
DOI: 10.17632/2wtz4cfzzs
title: Sex-Specific Signal Processing of Hematocrit (PCV) Values: A Novel System for Diagnosing Haematological Conditions and Population Health Screening

description: A dataset for Sex-Specific Signal Processing of Hematocrit (PCV) Values: A Novel System for Diagnosing Haematological Conditions and Population Health Screening. This study utilized de-identified data sourced from the health medical record unit of Bowen University Hospital. Ethical approval was requested and granted by the Directorate of Research and Strategic Partnerships at Bowen University.
DOI: 10.17632/2wtz4cfzzs.1
title: Sex-Specific Signal Processing of Hematocrit (PCV) Values: A Novel System for Diagnosing Haematological Conditions and Population Health Screening

description: A dataset for Sex-Specific Signal Processing of Hematocrit (PCV) Values: A Novel System for Diagnosing Haematological Conditions and Population Health Screening. This study utilized de-identified data sourced from the health medical record unit of Bowen University Hospital. Ethical approval was requested and granted by the Directorate of Research and Strategic Partnerships at Bowen University.
DOI: 10.17632/pjztc9gxbc
title: Prevention of Lymphoceles Using Peritoneal Flaps During Robotic Assisted Radical Prostatectomy with Pelvic Lymph Node Dissection: A Systematic Review and Meta-analysis

description: This dataset was generated as part of a systematic review and meta-analysis investigating whether the use of a peritoneal flap during robotic-assisted radical prostatectomy with pelvic lymph node dissection reduces the incidence of lymphoceles and related complications. The table contains study-level data extracted from randomized controlled trials and observational studies, including details on study design, patient characteristics, surgical technique, and reported outcomes. Data were gathered through a structured extraction process conducted independently by two reviewers, with discrepancies resolved by consensus.
DOI: 10.17632/pjztc9gxbc.1
title: Prevention of Lymphoceles Using Peritoneal Flaps During Robotic Assisted Radical Prostatectomy with Pelvic Lymph Node Dissection: A Systematic Review and Meta-analysis

description: This dataset was generated as part of a systematic review and meta-analysis investigating whether the use of a peritoneal flap during robotic-assisted radical prostatectomy with pelvic lymph node dissection reduces the incidence of lymphoceles and related complications. The table contains study-level data extracted from randomized controlled trials and observational studies, including details on study design, patient characteristics, surgical technique, and reported outcomes. Data were gathered through a structured extraction process conducted independently by two reviewers, with discrepancies resolved by consensus.
DOI: 10.17632/t49v8kn4t5
title: Dietary sucralose may affect lipid and glucose metabolism by reducing islet β cells function

description: This dataset belongs to the article titled ' Dietary sucralose may affect lipid and glucose metabolism by reducing islet β cells function '. The specific grouping and processing factors have been introduced in the article.It should be noted that at the beginning of randomization, there were 8 mice in each group. However, the number of data in the database is not all 8, which is due to the removal of outliers and the death of experimental mice. The judgment of outliers is obtained by spss software for box plot. These uploaded data are consistent with the data for statistical analysis and drawing in the paper.
DOI: 10.34740/kaggle/dsv/12932204
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.17632/t49v8kn4t5.1
title: Dietary sucralose may affect lipid and glucose metabolism by reducing islet β cells function

description: This dataset belongs to the article titled ' Dietary sucralose may affect lipid and glucose metabolism by reducing islet β cells function '. The specific grouping and processing factors have been introduced in the article.It should be noted that at the beginning of randomization, there were 8 mice in each group. However, the number of data in the database is not all 8, which is due to the removal of outliers and the death of experimental mice. The judgment of outliers is obtained by spss software for box plot. These uploaded data are consistent with the data for statistical analysis and drawing in the paper.
DOI: 10.17632/hphz368xff
title: Replication Folder for "Does digital finance spatial correlation drive income convergence?"

description: The dataset is used for the study Does digital finance spatial correlation drive income convergence?
DOI: 10.17632/hphz368xff.1
title: Replication Folder for "Does digital finance spatial correlation drive income convergence?"

description: The dataset is used for the study Does digital finance spatial correlation drive income convergence?
DOI: 10.17632/wt3svg7z89
title: Dataset for "Predicting the Finished Fabric Width and Areal Density (Grams per Square Meter) of Commercially Produced Plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)"

description: This dataset contains the data used in the research titled “Predicting the Finished Fabric Width and Areal Density (Grams per Square Meter) of Commercially Produced Plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)”. The data was collected from mass production in an industrial environment and consists of key input parameters and corresponding fabric characteristics.

The dataset includes the following files:
1) Raw_Data.xlsx – The original dataset containing more than 30,000 observations collected from commercial production of 100% cotton plain single jersey knitted fabric. Each record includes Stitch Length (SL), Yarn Count (YC), Machine Diameter (D), Finished Fabric Width (FW), and Areal Density (GSM).

2) Training_Data.xlsx – A subset of the raw data used for building and training the fuzzy inference system (FIS) model. This dataset includes input parameters (SL, YC, D) and corresponding outputs (FW, GSM). The Subset was created using the "random" library of python. 

3) Testing_Data.xlsx – Data used to test and validate the accuracy of the trained fuzzy model. This file contains unseen data points (not included in training) with the same input and output parameters. The raw data was separated into Training Data and Testing Data using python "random" library. 

4) 55_Developed_Fabric_Data.xlsx – Real-world experimental observations collected from controlled production runs, specifically designed for model verification and reliability assessment.

Purpose of the Dataset
The dataset supports research on predicting critical fabric properties (Finished Width and Areal Density) using fuzzy logic-based modeling. It enables further studies, replication of results, and development of predictive tools in the textile manufacturing domain.

Key Variables:
SL: Stitch Length (for 10Loops in mm)
YC: Yarn Fineness (Ne)
D: Machine Diameter (inch)

FW: Finished Fabric Width (inch)
GSM: Areal Density (GSM)

Data Format: Excel (.xlsx)

Number of Records:
Raw Data: ~30,000 observations
Training Data: Random subsets of the raw data (~24,000)
Testing Data: Random subsets of the raw data (~7,000)
Experimental Data: Independent validation dataset (55 Dataset)
DOI: 10.17632/wt3svg7z89.1
title: Dataset for "Predicting the Finished Fabric Width and Areal Density (Grams per Square Meter) of Commercially Produced Plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)"

description: This dataset contains the data used in the research titled “Predicting the Finished Fabric Width and Areal Density (Grams per Square Meter) of Commercially Produced Plain Single Jersey (100% Cotton) Knitted Fabric using Fuzzy Inference System (FIS)”. The data was collected from mass production in an industrial environment and consists of key input parameters and corresponding fabric characteristics.

The dataset includes the following files:
1) Raw_Data.xlsx – The original dataset containing more than 30,000 observations collected from commercial production of 100% cotton plain single jersey knitted fabric. Each record includes Stitch Length (SL), Yarn Count (YC), Machine Diameter (D), Finished Fabric Width (FW), and Areal Density (GSM).

2) Training_Data.xlsx – A subset of the raw data used for building and training the fuzzy inference system (FIS) model. This dataset includes input parameters (SL, YC, D) and corresponding outputs (FW, GSM). The Subset was created using the "random" library of python. 

3) Testing_Data.xlsx – Data used to test and validate the accuracy of the trained fuzzy model. This file contains unseen data points (not included in training) with the same input and output parameters. The raw data was separated into Training Data and Testing Data using python "random" library. 

4) 55_Developed_Fabric_Data.xlsx – Real-world experimental observations collected from controlled production runs, specifically designed for model verification and reliability assessment.

Purpose of the Dataset
The dataset supports research on predicting critical fabric properties (Finished Width and Areal Density) using fuzzy logic-based modeling. It enables further studies, replication of results, and development of predictive tools in the textile manufacturing domain.

Key Variables:
SL: Stitch Length (for 10Loops in mm)
YC: Yarn Fineness (Ne)
D: Machine Diameter (inch)

FW: Finished Fabric Width (inch)
GSM: Areal Density (GSM)

Data Format: Excel (.xlsx)

Number of Records:
Raw Data: ~30,000 observations
Training Data: Random subsets of the raw data (~24,000)
Testing Data: Random subsets of the raw data (~7,000)
Experimental Data: Independent validation dataset (55 Dataset)
DOI: 10.17632/t8k59p35p6
title: Expert Annotated Mandibular Third Molar (ExAn-MTM) Dataset

description: 📌 Overview of ExAn-MTM Dataset 

The ExAn-MTM dataset was developed to address the scarcity of publicly available annotated datasets for mandibular third molar (MTM) detection. It is derived from high-quality panoramic radiographs (PRs) and consists of 973 expertly annotated images, adapted from the original m-TM dataset.

This dataset provides a reliable benchmark for AI-driven dental diagnostics, computer vision research, and medical imaging studies.

📂Dataset Structure- The dataset includes bounding box annotations for two clinically important classes of mandibular third molars:

0 → e-MTM (erupted mandibular third molar)

1 → i-MTM (impacted mandibular third molar)

Annotations were created by an experienced oral and maxillofacial radiologist using the MakeSense annotation tool, and are provided in YOLO format.

Folder structure:

/ExAn-MTM dataset/
├─ train/
│ ├─ images/
│ ├─ labels/
├─ valid/
│ ├─ images/
│ ├─ labels/

Data split:

Training Set → 875 images (624 e-MTM, 980 i-MTM)

Validation Set → 98 images (63 e-MTM, 117 i-MTM)

🎯 Applications

The dataset can be used for:

Training and validating object detection models (YOLO, Faster R-CNN, RetinaNet, etc.)

Benchmarking AI-based clinical decision support systems (CDSS) in dentistry

Conducting comparative studies in medical imaging and annotation methods

Advancing research in explainable AI (XAI) for dental applications

🌍 Significance

First publicly available, expert-annotated dataset dedicated to mandibular third molar detection.

Adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable).

Enhances reproducibility and transparency in dental AI research.

Provides a solid foundation for developing and benchmarking diagnostic models in dental radiology.

📑 Citation

This dataset is derived from and must be cited alongside the following studies:

1- Kayadibi, İ., Köse, U., Güraksın, G.E. et al. E-MTMYOLO: an explainable YOLOv5-based architecture for accurate detection of mandibular third molar using a novel expert-annotated dataset. J Supercomput 81, 1286 (2025). https://doi.org/10.1007/s11227-025-07775-w

2- Kayadibi, İ., Köse, U., Güraksın, G. E., & Çetin, B. (2025). An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography. International Journal of Medical Informatics, 195, 105724. https://doi.org/10.1016/j.ijmedinf.2024.105724
DOI: 10.17632/t8k59p35p6.1
title: Expert Annotated Mandibular Third Molar (ExAn-MTM) Dataset

description: 📌 Overview of ExAn-MTM Dataset 

The ExAn-MTM dataset was developed to address the scarcity of publicly available annotated datasets for mandibular third molar (MTM) detection. It is derived from high-quality panoramic radiographs (PRs) and consists of 973 expertly annotated images, adapted from the original m-TM dataset.

This dataset provides a reliable benchmark for AI-driven dental diagnostics, computer vision research, and medical imaging studies.

📂Dataset Structure- The dataset includes bounding box annotations for two clinically important classes of mandibular third molars:

0 → e-MTM (erupted mandibular third molar)

1 → i-MTM (impacted mandibular third molar)

Annotations were created by an experienced oral and maxillofacial radiologist using the MakeSense annotation tool, and are provided in YOLO format.

Folder structure:

/ExAn-MTM dataset/
├─ train/
│ ├─ images/
│ ├─ labels/
├─ valid/
│ ├─ images/
│ ├─ labels/

Data split:

Training Set → 875 images (624 e-MTM, 980 i-MTM)

Validation Set → 98 images (63 e-MTM, 117 i-MTM)

🎯 Applications

The dataset can be used for:

Training and validating object detection models (YOLO, Faster R-CNN, RetinaNet, etc.)

Benchmarking AI-based clinical decision support systems (CDSS) in dentistry

Conducting comparative studies in medical imaging and annotation methods

Advancing research in explainable AI (XAI) for dental applications

🌍 Significance

First publicly available, expert-annotated dataset dedicated to mandibular third molar detection.

Adheres to FAIR principles (Findable, Accessible, Interoperable, Reusable).

Enhances reproducibility and transparency in dental AI research.

Provides a solid foundation for developing and benchmarking diagnostic models in dental radiology.

📑 Citation

This dataset is derived from and must be cited alongside the following studies:

1- Kayadibi, İ., Köse, U., Güraksın, G.E. et al. E-MTMYOLO: an explainable YOLOv5-based architecture for accurate detection of mandibular third molar using a novel expert-annotated dataset. J Supercomput 81, 1286 (2025). https://doi.org/10.1007/s11227-025-07775-w

2- Kayadibi, İ., Köse, U., Güraksın, G. E., & Çetin, B. (2025). An AI-assisted explainable mTMCNN architecture for detection of mandibular third molar presence from panoramic radiography. International Journal of Medical Informatics, 195, 105724. https://doi.org/10.1016/j.ijmedinf.2024.105724
DOI: 10.5281/zenodo.17022002
title: 7009X Evidence Base

description: The evidence base used during the development of the IEEE Standard for Fail-Safe Design of Autonomous and Semi-Autonomous Systems (7009-2024) . The evidence base is now being used to develop the first extension (X1) derived from the 7009 standard.  An accompanying document (7009X-EBI.pdf) provides a brief introduction to the baseline release of the evidence base (V2.1).
DOI: 10.5281/zenodo.17021963
title: Comprehensive Patterns of Pediatric Upper Extremity Fractures: Age, Sex, and Seasonal Variations in a 15-Year Cohort

description: The dataset contains pediatric upper extremity fracture records collected over a 15-year period. The Excel file includes raw patient-level data and summary tables stratified by season (spring, summer, autumn, winter) and by age groups (0–3, 4–7, 8–13, 14–18 years). The main sheets  present anonymized case-level data with variables such as age, sex, fracture type, and date of injury. The aggregated sheets provide summary statistics for seasonal and age-specific fracture distributions. This dataset was generated for an epidemiological analysis of fracture patterns in children and adolescents and may be useful for further research on pediatric trauma epidemiology.