cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/ngc58fsxgd
title: BrinjalFruitX: A Field-Collected Image Dataset for Machine Learning and Deep Learning-Based Disease Identification in Brinjal Fruits

description: This dataset comprises a total of 1,823 high-quality images of brinjal (eggplant) fruits, annotated across five distinct classes representing both healthy and disease-infected conditions. The dataset was curated with a strong emphasis on real-world variability, agricultural relevance, and class diversity to support robust machine learning (ML), deep learning (DL), and computer vision (CV) applications in plant disease detection.

Class Distribution:
The five categories and their respective image counts are as follows:

1. Shoot and Fruit Borer: 725 images
2. Healthy: 514 images
3. Wet Rot: 223 images
4. Brinjal Fruit Cracking: 200 images
5. Phomopsis Blight: 161 images

The class distribution reflects natural occurrences in the field, thereby introducing a realistic imbalance that is often encountered in agricultural datasets. This also provides an opportunity to test model robustness under skewed class conditions.

Data Collection Methodology
To ensure data quality and diversity, all images were collected manually from actual agricultural fields during extensive field visits across two major brinjal-producing regions in Bangladesh: Bogura and Dhaka. These regions were selected due to their prominence in vegetable farming and accessibility to disease-affected brinjal crops. Images were captured using a standard smartphone camera under natural lighting conditions, without artificial augmentation or pre-processing during acquisition. A total of 2,273 raw images were initially collected from the field, and after rigorous quality filtering, labeling, and expert validation, 1,823 images were finalized for the dataset.

Characteristics:
- Image Format: JPEG
- Image Size: Varied; later resized uniformly for model input (typically 128×128 or 224×224 pixels)
- Capture Device: Smartphone cameras
 - Environment: Natural daylight, outdoor agricultural settings
- Data Source: Real field samples from farmers' brinjal crops
- Annotation: Manual expert labeling under the guidance of agricultural specialists

Significance and Applications
This dataset provides a realistic, high-variance visual resource for developing and testing machine learning and deep learning models for plant disease recognition. Its real-world origin ensures relevance in practical agricultural applications, while its diversity and class-specific challenges make it suitable for experimentation in:

1. Supervised classification tasks
2. Transfer learning and fine-tuning of vision models
3. Imbalanced learning techniques (e.g., oversampling, undersampling)
4. Explainable AI (XAI) and model interpretation (e.g., Grad-CAM)
5. Lightweight model deployment for mobile-based disease detection tools
DOI: 10.15468/dl.mquptt
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Devauxia fluitans, Devauxia fluitans, Festuca fluitans, Festucaria heisteri, Festucaria heisteri, Glyceria acuminata, Glyceria denticulata, Glyceria fluitans triticea, Glyceria fluitans poiformis, Glyceria fluitans acutiflora)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019175-250711103210423/datasets/export for details.
DOI: 10.17632/ngc58fsxgd.1
title: BrinjalFruitX: A Field-Collected Image Dataset for Machine Learning and Deep Learning-Based Disease Identification in Brinjal Fruits

description: This dataset comprises a total of 1,823 high-quality images of brinjal (eggplant) fruits, annotated across five distinct classes representing both healthy and disease-infected conditions. The dataset was curated with a strong emphasis on real-world variability, agricultural relevance, and class diversity to support robust machine learning (ML), deep learning (DL), and computer vision (CV) applications in plant disease detection.

Class Distribution:
The five categories and their respective image counts are as follows:

1. Shoot and Fruit Borer: 725 images
2. Healthy: 514 images
3. Wet Rot: 223 images
4. Brinjal Fruit Cracking: 200 images
5. Phomopsis Blight: 161 images

The class distribution reflects natural occurrences in the field, thereby introducing a realistic imbalance that is often encountered in agricultural datasets. This also provides an opportunity to test model robustness under skewed class conditions.

Data Collection Methodology
To ensure data quality and diversity, all images were collected manually from actual agricultural fields during extensive field visits across two major brinjal-producing regions in Bangladesh: Bogura and Dhaka. These regions were selected due to their prominence in vegetable farming and accessibility to disease-affected brinjal crops. Images were captured using a standard smartphone camera under natural lighting conditions, without artificial augmentation or pre-processing during acquisition. A total of 2,273 raw images were initially collected from the field, and after rigorous quality filtering, labeling, and expert validation, 1,823 images were finalized for the dataset.

Characteristics:
- Image Format: JPEG
- Image Size: Varied; later resized uniformly for model input (typically 128×128 or 224×224 pixels)
- Capture Device: Smartphone cameras
 - Environment: Natural daylight, outdoor agricultural settings
- Data Source: Real field samples from farmers' brinjal crops
- Annotation: Manual expert labeling under the guidance of agricultural specialists

Significance and Applications
This dataset provides a realistic, high-variance visual resource for developing and testing machine learning and deep learning models for plant disease recognition. Its real-world origin ensures relevance in practical agricultural applications, while its diversity and class-specific challenges make it suitable for experimentation in:

1. Supervised classification tasks
2. Transfer learning and fine-tuning of vision models
3. Imbalanced learning techniques (e.g., oversampling, undersampling)
4. Explainable AI (XAI) and model interpretation (e.g., Grad-CAM)
5. Lightweight model deployment for mobile-based disease detection tools
DOI: 10.17632/z2wvwk3j5m
title: 1000+ Beam Test Data of Fiber Reinforced Concrete

description: This dataset comprises flexural beam test results from over 1,000 fiber-reinforced concrete specimens tested at both 1-day and 28-day curing intervals. Each entry includes detailed load-deflection curves, concrete mix properties, fiber characteristics, geometric dimensions, and associated failure modes. The data captures a broad spectrum of material behaviors across varying reinforcement ratios, fiber volumes, and concrete strengths. This resource is highly valuable for training AI models that predict flexural performance, classify post-cracking responses, or extract material properties through inverse analysis. It also enables the development of data-driven constitutive models and supports automated calibration of numerical simulations. The structure of the data is suitable for graph-based representations, where each beam can be modeled as a node with edges capturing similarity in mechanical behavior, mix design, or fracture mode. This opens new avenues for graph theory applications such as clustering, anomaly detection, and path-based learning, allowing researchers to explore hidden patterns, failure evolution, and topology-informed material design strategies.
DOI: 10.17632/z2wvwk3j5m.1
title: 1000+ Beam Test Data of Fiber Reinforced Concrete

description: This dataset comprises flexural beam test results from over 1,000 fiber-reinforced concrete specimens tested at both 1-day and 28-day curing intervals. Each entry includes detailed load-deflection curves, concrete mix properties, fiber characteristics, geometric dimensions, and associated failure modes. The data captures a broad spectrum of material behaviors across varying reinforcement ratios, fiber volumes, and concrete strengths. This resource is highly valuable for training AI models that predict flexural performance, classify post-cracking responses, or extract material properties through inverse analysis. It also enables the development of data-driven constitutive models and supports automated calibration of numerical simulations. The structure of the data is suitable for graph-based representations, where each beam can be modeled as a node with edges capturing similarity in mechanical behavior, mix design, or fracture mode. This opens new avenues for graph theory applications such as clustering, anomaly detection, and path-based learning, allowing researchers to explore hidden patterns, failure evolution, and topology-informed material design strategies.
DOI: 10.17632/8s33zdj2gy
title: JA wheat pink stem borer

description: This dataset supports the findings of the research article titled “Synthetic Elicitor- and Herbivory-Induced Volatiles in Wheat Mediate Pink Stem Borer Suppression and Trichogramma chilonis Attraction”. It includes raw and semi-processed data from field and laboratory experiments conducted during the 2021–22 and 2022–23 winter seasons. The study investigated the efficacy of foliar application of plant defense elicitors—primarily jasmonic acid (JA)—in reducing wheat damage caused by the pink stem borer (Sesamia inferens) and in modulating volatile organic compounds (VOCs) that attract the egg parasitoid Trichogramma chilonis.

The data are organized into the following components:

Field trial data: Measurements of pest-induced damage (dead heart and white ear symptoms) and grain yield under different elicitor treatments.

GC-MS raw outputs: Volatile profiles of healthy, herbivore-infested, and JA-treated wheat plants.

Volatile compound identification: Peak lists, retention times, and compound identities derived from GC-MS.

Bioassay data: Behavioral responses of T. chilonis to synthetic volatiles, including parasitism rates at various concentrations.

Supplementary metadata: Explanation of treatments, experimental design, compound concentrations, and parasitism scoring.

The dataset is intended to provide transparency, enable replication, and facilitate future meta-analyses or modeling work related to induced plant defenses and chemical ecology in cereal crops.

Keywords: Wheat, Sesamia inferens, Jasmonic acid, Plant elicitors, Herbivore-induced plant volatiles (HIPVs), Trichogramma chilonis, GC-MS, Bioassay, Biological control, Integrated pest management
DOI: 10.17632/8s33zdj2gy.1
title: JA wheat pink stem borer

description: This dataset supports the findings of the research article titled “Synthetic Elicitor- and Herbivory-Induced Volatiles in Wheat Mediate Pink Stem Borer Suppression and Trichogramma chilonis Attraction”. It includes raw and semi-processed data from field and laboratory experiments conducted during the 2021–22 and 2022–23 winter seasons. The study investigated the efficacy of foliar application of plant defense elicitors—primarily jasmonic acid (JA)—in reducing wheat damage caused by the pink stem borer (Sesamia inferens) and in modulating volatile organic compounds (VOCs) that attract the egg parasitoid Trichogramma chilonis.

The data are organized into the following components:

Field trial data: Measurements of pest-induced damage (dead heart and white ear symptoms) and grain yield under different elicitor treatments.

GC-MS raw outputs: Volatile profiles of healthy, herbivore-infested, and JA-treated wheat plants.

Volatile compound identification: Peak lists, retention times, and compound identities derived from GC-MS.

Bioassay data: Behavioral responses of T. chilonis to synthetic volatiles, including parasitism rates at various concentrations.

Supplementary metadata: Explanation of treatments, experimental design, compound concentrations, and parasitism scoring.

The dataset is intended to provide transparency, enable replication, and facilitate future meta-analyses or modeling work related to induced plant defenses and chemical ecology in cereal crops.

Keywords: Wheat, Sesamia inferens, Jasmonic acid, Plant elicitors, Herbivore-induced plant volatiles (HIPVs), Trichogramma chilonis, GC-MS, Bioassay, Biological control, Integrated pest management
DOI: 10.17632/gz7n94f2dp
title: Diverse production systems affect soil phosphorus forms and soybean and cotton productivity in tropical soil

description: Data used for the statistical analysis of the study
DOI: 10.17632/gz7n94f2dp.1
title: Diverse production systems affect soil phosphorus forms and soybean and cotton productivity in tropical soil

description: Data used for the statistical analysis of the study
DOI: 10.17632/g2trx5cr8r
title: Epigenetic changes associated with obesity-related metabolic comorbidities - Supplemental Data

description: Epigenetic changes associated with obesity-related metabolic comorbidities - Supplemental Data
DOI: 10.15468/dl.ccg6ex
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ondatra zibethicus",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 41 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019176-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g2trx5cr8r.1
title: Epigenetic changes associated with obesity-related metabolic comorbidities - Supplemental Data

description: Epigenetic changes associated with obesity-related metabolic comorbidities - Supplemental Data
DOI: 10.17632/64bvbr63f6
title: Instances for Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport

description: This dataset supports the study "Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport". It consists of instances generated for the Pickup and Delivery Problem with Time Windows scheduled line services, following the problem proposed by Ghilas et al. (2018). Each instances consists of number of vehicles with capacity and its depot, requests, fixed scheduled line service departure time, node location and time window. 
The dataset is organized according to the corresponding sections of the study:
Section 6: Contains artificially generated instances based on the framework of Ghilas et al. (2018). These are grouped into five distinct folders, as illustrated and explained in our study.
Section 7: Case Study: For the real-world case study, we adopt the Berlin dataset of 5000 requests from Sartori and Buriol (2020).

Additionally, a detailed description of the instance sets is provided in "Description.txt" to facilitate understanding and reproducibility.


References:
1. Tundulyasaree, K., Martin, L., van Lieshout, R. N., & Van Woensel, T. (2025). Optimal taxes and subsidies to incentivize modal shift for inner-city freight transport. arXiv preprint arXiv:2501.09467.
2. Ghilas, V., Cordeau, J. F., Demir, E., & Van Woensel, T. (2018). Branch-and-price for the pickup and delivery problem with time windows and scheduled lines. Transportation Science, 52(5), 1191–1210. 
3. Sartori, C. S., & Buriol, L. S. (2020). A study on the pickup and delivery problem with time windows: Matheuristics and new instances. Computers and Operations Research, 124, 105065.
DOI: 10.17632/64bvbr63f6.1
title: Instances for Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport

description: This dataset supports the study "Optimal Taxes and Subsidies to Incentivize Modal Shift for Inner-city Freight Transport". It consists of instances generated for the Pickup and Delivery Problem with Time Windows scheduled line services, following the problem proposed by Ghilas et al. (2018). Each instances consists of number of vehicles with capacity and its depot, requests, fixed scheduled line service departure time, node location and time window. 
The dataset is organized according to the corresponding sections of the study:
Section 6: Contains artificially generated instances based on the framework of Ghilas et al. (2018). These are grouped into five distinct folders, as illustrated and explained in our study.
Section 7: Case Study: For the real-world case study, we adopt the Berlin dataset of 5000 requests from Sartori and Buriol (2020).

Additionally, a detailed description of the instance sets is provided in "Description.txt" to facilitate understanding and reproducibility.


References:
1. Tundulyasaree, K., Martin, L., van Lieshout, R. N., & Van Woensel, T. (2025). Optimal taxes and subsidies to incentivize modal shift for inner-city freight transport. arXiv preprint arXiv:2501.09467.
2. Ghilas, V., Cordeau, J. F., Demir, E., & Van Woensel, T. (2018). Branch-and-price for the pickup and delivery problem with time windows and scheduled lines. Transportation Science, 52(5), 1191–1210. 
3. Sartori, C. S., & Buriol, L. S. (2020). A study on the pickup and delivery problem with time windows: Matheuristics and new instances. Computers and Operations Research, 124, 105065.
DOI: 10.17632/cgttbvkrty
title: dados_normalizados_brumadinho

description: The files contain the data analyzed in the article: "SOIL PHYSICAL QUALITY IN AREAS AFFECTED BY FLOODING AND ALLUVIAL SEDIMENT DEPOSITION ALONG THE BANKS OF THE PARAOPEBA RIVER, MG", to obtain multivariate statistics: PCA and factorial. We also include the correlation matrix. 

Correlations were classified into three groups: strong positive correlation (values from 0.7 to 0.99, highlighted in green), strong negative correlation (values from -0.7 to -0.99, highlighted in red), and little or no correlation (values &gt;-0.7 and &lt;0.7, highlighted in yellow).
DOI: 10.17632/cgttbvkrty.1
title: dados_normalizados_brumadinho

description: The files contain the data analyzed in the article: "SOIL PHYSICAL QUALITY IN AREAS AFFECTED BY FLOODING AND ALLUVIAL SEDIMENT DEPOSITION ALONG THE BANKS OF THE PARAOPEBA RIVER, MG", to obtain multivariate statistics: PCA and factorial. We also include the correlation matrix. 

Correlations were classified into three groups: strong positive correlation (values from 0.7 to 0.99, highlighted in green), strong negative correlation (values from -0.7 to -0.99, highlighted in red), and little or no correlation (values &gt;-0.7 and &lt;0.7, highlighted in yellow).
DOI: 10.17632/dgp5xp5nxc
title: A Study on the Role of Business Analytics in Shaping Brand Promotion Strategies

description: This study examines the role of business analytics in shaping brand promotion strategies, focusing on three key areas: the enhancement of brand positioning through advanced analytics techniques, the impact of big data analytics on the personalization of promotional content, and the role of collaboration between analytics and marketing teams in developing cohesive campaigns. The primary objectives were to determine if machine learning and AI-based models significantly enhance brand positioning and competitive differentiation, assess how big data analytics influences consumer loyalty and satisfaction through personalized promotions, and investigate the effectiveness of cross-functional collaboration in brand promotion. Employing a predominantly quantitative approach with minimal qualitative input for context, data were collected via structured questionnaires administered through Google Forms to marketing professionals and analysts. Descriptive and inferential statistical analyses—including T-tests, correlation, and regression— were conducted to test the hypotheses and explore relationships among key variables. Findings reveal that while advanced analytics techniques did not significantly impact brand positioning, a significant albeit weak positive correlation exists between big data analytics and personalized promotional content, enhancing consumer satisfaction. Furthermore, regression analysis indicates that collaboration between analytics and marketing teams significantly improves campaign cohesiveness. These insights suggest that refining data modeling approaches, adopting advanced machine learning algorithms, and fostering stronger cross-functional collaboration could enhance the efficacy of analytics-driven brand promotion strategies. Future research should consider larger, industry-specific samples and longitudinal studies to further elucidate these dynamics.
DOI: 10.17632/dgp5xp5nxc.1
title: A Study on the Role of Business Analytics in Shaping Brand Promotion Strategies

description: This study examines the role of business analytics in shaping brand promotion strategies, focusing on three key areas: the enhancement of brand positioning through advanced analytics techniques, the impact of big data analytics on the personalization of promotional content, and the role of collaboration between analytics and marketing teams in developing cohesive campaigns. The primary objectives were to determine if machine learning and AI-based models significantly enhance brand positioning and competitive differentiation, assess how big data analytics influences consumer loyalty and satisfaction through personalized promotions, and investigate the effectiveness of cross-functional collaboration in brand promotion. Employing a predominantly quantitative approach with minimal qualitative input for context, data were collected via structured questionnaires administered through Google Forms to marketing professionals and analysts. Descriptive and inferential statistical analyses—including T-tests, correlation, and regression— were conducted to test the hypotheses and explore relationships among key variables. Findings reveal that while advanced analytics techniques did not significantly impact brand positioning, a significant albeit weak positive correlation exists between big data analytics and personalized promotional content, enhancing consumer satisfaction. Furthermore, regression analysis indicates that collaboration between analytics and marketing teams significantly improves campaign cohesiveness. These insights suggest that refining data modeling approaches, adopting advanced machine learning algorithms, and fostering stronger cross-functional collaboration could enhance the efficacy of analytics-driven brand promotion strategies. Future research should consider larger, industry-specific samples and longitudinal studies to further elucidate these dynamics.
DOI: 10.15468/dl.rmn7nd
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xanthomonas axonopodis",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019177-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xhh9cw
title: Occurrence Download

description: A dataset containing 748 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ondatra zibethicus",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 748 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019178-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.