cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/yrp9z4pwss
title: Data from: Interactive Effects of Air and Nutrient Solution Temperature and CO2 Concentration on Growth, Yield, and Quality of Fresh Cut Culinary Herbs

description: Raw data from: Interactive Effects of Air and Nutrient Solution Temperature and CO2 Concentration on Growth, Yield, and Quality of Fresh Cut Culinary Herbs

Includes all measurements from all measurable plants taken including width 1, width 2, height, chlorophyll fluorescence, shoot fresh mass, shoot dry mass, Root dry mass, leaf area, growth index, and leaf-area ratio
DOI: 10.17632/yrp9z4pwss.1
title: Data from: Interactive Effects of Air and Nutrient Solution Temperature and CO2 Concentration on Growth, Yield, and Quality of Fresh Cut Culinary Herbs

description: Raw data from: Interactive Effects of Air and Nutrient Solution Temperature and CO2 Concentration on Growth, Yield, and Quality of Fresh Cut Culinary Herbs

Includes all measurements from all measurable plants taken including width 1, width 2, height, chlorophyll fluorescence, shoot fresh mass, shoot dry mass, Root dry mass, leaf area, growth index, and leaf-area ratio
DOI: 10.17632/h2ytgy2pmb
title: Fabric Anomaly Recognition Dataset

description: This dataset has been specifically developed to address critical challenges in fabric defect detection within the textile industry of Bangladesh. Recognizing the importance of maintaining high-quality standards in textile exports, minimizing material waste, and ensuring customer satisfaction, this dataset offers a comprehensive, real-world collection of fabric defects captured directly on production floors in Bangladeshi textile manufacturing units.
The dataset focuses on three principal defect types that are most prevalent and industrially significant in textile quality control:
• Hole (Class ID-0): Physical punctures or missing material resulting from mechanical damage during weaving, finishing, or handling stages. These defects undermine both the fabric’s structural integrity and its visual appeal, often leading to rejection of the product.
• Broken End (Class ID-1): Breakage of warp yarns during weaving or finishing, causing visible gaps or thin lines on the fabric surface. These occur due to mechanical failures, excessive yarn tension, or handling errors, affecting fabric durability.
• Oil Stains (Class ID-2): Surface discolorations and blemishes caused by oil or lubricant leaks from machinery during production. Such stains can alter fabric appearance and interfere with subsequent dyeing or finishing operations.
The dataset consists of a total of 2,157 images, carefully curated to maintain near-class balance and reduce bias during machine learning model development. The class distribution is:
• Hole: 649 images
• Broken End: 666 images
• Oil Stains: 842 images
This balanced representation ensures that models trained on this dataset can detect defects equitably across all classes, enhancing overall performance.
All images were collected in situ on factory floors across various textile units in Bangladesh, capturing defects in authentic production environments. Images exhibit a wide range of illumination conditions and background variations, reflecting real operational challenges encountered in industrial settings.
Annotation was performed using LabelImg, a widely adopted open-source graphical tool for object detection labeling. Expert annotators manually drew bounding boxes tightly enclosing each defect instance, providing precise spatial localization critical for training and validation of deep learning models. Each annotated object is labeled with a numerical class ID consistent with widely used detection model conventions, such as YOLO:
• Hole → 0
• Broken End → 1
• Oil Stains → 2
The labeling process involved rigorous quality control, including multiple review cycles and cross-validation by different annotators, to minimize subjective errors and ensure annotation accuracy. This meticulous annotation effort is essential to prevent error propagation during model training, which could otherwise degrade detection performance.
DOI: 10.17632/h2ytgy2pmb.1
title: Fabric Anomaly Recognition Dataset

description: This dataset has been specifically developed to address critical challenges in fabric defect detection within the textile industry of Bangladesh. Recognizing the importance of maintaining high-quality standards in textile exports, minimizing material waste, and ensuring customer satisfaction, this dataset offers a comprehensive, real-world collection of fabric defects captured directly on production floors in Bangladeshi textile manufacturing units.
The dataset focuses on three principal defect types that are most prevalent and industrially significant in textile quality control:
• Hole (Class ID-0): Physical punctures or missing material resulting from mechanical damage during weaving, finishing, or handling stages. These defects undermine both the fabric’s structural integrity and its visual appeal, often leading to rejection of the product.
• Broken End (Class ID-1): Breakage of warp yarns during weaving or finishing, causing visible gaps or thin lines on the fabric surface. These occur due to mechanical failures, excessive yarn tension, or handling errors, affecting fabric durability.
• Oil Stains (Class ID-2): Surface discolorations and blemishes caused by oil or lubricant leaks from machinery during production. Such stains can alter fabric appearance and interfere with subsequent dyeing or finishing operations.
The dataset consists of a total of 2,157 images, carefully curated to maintain near-class balance and reduce bias during machine learning model development. The class distribution is:
• Hole: 649 images
• Broken End: 666 images
• Oil Stains: 842 images
This balanced representation ensures that models trained on this dataset can detect defects equitably across all classes, enhancing overall performance.
All images were collected in situ on factory floors across various textile units in Bangladesh, capturing defects in authentic production environments. Images exhibit a wide range of illumination conditions and background variations, reflecting real operational challenges encountered in industrial settings.
Annotation was performed using LabelImg, a widely adopted open-source graphical tool for object detection labeling. Expert annotators manually drew bounding boxes tightly enclosing each defect instance, providing precise spatial localization critical for training and validation of deep learning models. Each annotated object is labeled with a numerical class ID consistent with widely used detection model conventions, such as YOLO:
• Hole → 0
• Broken End → 1
• Oil Stains → 2
The labeling process involved rigorous quality control, including multiple review cycles and cross-validation by different annotators, to minimize subjective errors and ensure annotation accuracy. This meticulous annotation effort is essential to prevent error propagation during model training, which could otherwise degrade detection performance.
DOI: 10.15468/dl.qneh84
title: Occurrence Download

description: A dataset containing 3277 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bradybaena similaris",
 "Country is United States of America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3277 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033330-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/f9cmx4g7jc
title: Supplemental Material

description: Supplemental Methods, Supplemental Table 1, Supplemental Table 2, and Supplemental Figure 1
DOI: 10.17632/f9cmx4g7jc.1
title: Supplemental Material

description: Supplemental Methods, Supplemental Table 1, Supplemental Table 2, and Supplemental Figure 1
DOI: 10.6084/m9.figshare.29971759.v1
title: Forecast uploaded on August 22 regarding the performance of Meta Platforms' stock ("On September 24, 2025, will Meta Platforms' stock price be higher than on August 23, 2025?")

description: Further details about the study are available at the following links:<br>https://osf.io/pcwah<br>https://osf.io/xsb9t
DOI: 10.6084/m9.figshare.29971759
title: Forecast uploaded on August 22 regarding the performance of Meta Platforms' stock ("On September 24, 2025, will Meta Platforms' stock price be higher than on August 23, 2025?")

description: Further details about the study are available at the following links:<br>https://osf.io/pcwah<br>https://osf.io/xsb9t
DOI: 10.17632/jkd6t3w635
title: Lemon Leaf Diseases Dataset and Annotations

description: The dataset contains eight classes of dominant diseases affecting lemon leaves such; Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Sooty Mould, Spider Mites as well as a class of Healthy leaves. This repository also contain annotations of all the classes and are converted into a .txt format which can be used for any artificial intelligence project and any agricultural research. This dataset is preprocessed to ensure high-quality input for disease detection, classification and segmentation.
DOI: 10.15468/dl.adk7nq
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bradybaena similaris",
 "Country is Vanuatu",
 "HasCoordinate is true"
 ]
}

 The dataset includes 30 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033331-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/jkd6t3w635.1
title: Lemon Leaf Diseases Dataset and Annotations

description: The dataset contains eight classes of dominant diseases affecting lemon leaves such; Anthracnose, Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Sooty Mould, Spider Mites as well as a class of Healthy leaves. This repository also contain annotations of all the classes and are converted into a .txt format which can be used for any artificial intelligence project and any agricultural research. This dataset is preprocessed to ensure high-quality input for disease detection, classification and segmentation.
DOI: 10.17632/ww9s44sk32
title: Dataset from a Systematic Review on State, Institutional, and Organizational Capacities in Disaster Risk Management

description: This dataset contains the results of a systematic literature review conducted for the article “State, Institutional, and Organizational Capacities in Disaster Risk Management”.

The dataset includes:

Search results retrieved from the Scopus and Web of Science platforms, including all records obtained through the defined search strategies.

Screened and eligible articles, after applying the inclusion and exclusion criteria.

Data extraction sheets, containing the coding and variables related to state, institutional, and organizational capacities, as well as the phases of disaster risk management addressed in each study.

The dataset allows other researchers to verify the review process, reproduce the selection and extraction steps, and reuse the data for further studies on disaster risk management capacities.
DOI: 10.17632/ww9s44sk32.1
title: Dataset from a Systematic Review on State, Institutional, and Organizational Capacities in Disaster Risk Management

description: This dataset contains the results of a systematic literature review conducted for the article “State, Institutional, and Organizational Capacities in Disaster Risk Management”.

The dataset includes:

Search results retrieved from the Scopus and Web of Science platforms, including all records obtained through the defined search strategies.

Screened and eligible articles, after applying the inclusion and exclusion criteria.

Data extraction sheets, containing the coding and variables related to state, institutional, and organizational capacities, as well as the phases of disaster risk management addressed in each study.

The dataset allows other researchers to verify the review process, reproduce the selection and extraction steps, and reuse the data for further studies on disaster risk management capacities.
DOI: 10.17632/z6g7hw2sfh.2
title: Lacticaseibacillus rhamnosus MDC 2012 whole-genome

description: Genetic and Biochemical Characterization of Lacticaseibacillus rhamnosus MDC 2012 and Its Potential in Processing of Raw Pork Meat
DOI: 10.17632/3x6yp5jdjc.1
title: Critical Role of Sea Spray Aerosol Production Pathways in Particulate Transfer Across the Sea-Air Interface

description: Sea spray aerosols (SSA) play a crucial role in transferring particulates from oceanic water to the atmosphere. The production pathways of SSA from bubble bursting include jet drops from bubble cavity collapse, surface film drops from bubble cap disintegration and collision film drops from underwater bubble collision. While the physical formation of these drops has been extensively studied, the mechanisms governing particle enrichment and transfer within these individual drop types remain poorly understood. This study systematically investigates particle enrichment and transfer across the three types of drops. While previous studies suggested only hydrophobic particles could be enriched by SSA, our findings reveal that both hydrophilic and hydrophobic particles can be enriched in all drop types, with surface film drops exhibiting significantly greater enrichment than previously reported. We propose for the first time that particle enrichment occurs during bubble film drainage because particles tend to remain within the thinning film rather than being uniformly drained out, leading to additional concentration within the film. These findings enhance our understanding of mass transfer at water-air interface.
DOI: 10.17632/5w6g2vpzgf
title: Toward scientific clarity in the evolutionary puzzle of Austropotamobius crayfish

description: Supporting datasets (mtCOI & 16S sequences ID, location and references) used in this study.
DOI: 10.17632/5w6g2vpzgf.3
title: Toward scientific clarity in the evolutionary puzzle of Austropotamobius crayfish

description: Supporting datasets (mtCOI & 16S sequences ID, location and references) used in this study.
DOI: 10.17632/h5fd7sr8yh
title: Calorie Consumption and Wages: Evidence from India’s Labor Market

description: This dataset and code generates the Tables and figures in the paper "Calorie Consumption and Wages: Evidence from India’s Labor Market," submitted to Economic Modelling.
DOI: 10.17632/h5fd7sr8yh.1
title: Calorie Consumption and Wages: Evidence from India’s Labor Market

description: This dataset and code generates the Tables and figures in the paper "Calorie Consumption and Wages: Evidence from India’s Labor Market," submitted to Economic Modelling.
DOI: 10.17632/ttyht4bypb
title: From Concentration to Flux: A Mechanistic Model for Accurate Methane Emission Estimates in Livestock

description: This dataset contains MATLAB source code used to implement the hybrid multiphysics framework described in the manuscript titled "A Physics-Based Framework for Accurate On-Farm Enteric Methane Sensing." The software models the complete emission-to-sensor pathway for methane released during cattle eructation events, integrating physiological control volume analysis with a transient buoyant puff model.

The main script is MassConservationV05.m, which simulates methane transport through four interconnected control volumes representing the rumen, lungs, local environment, and sampling device. It applies the Reynolds Transport Theorem to derive time-resolved methane concentrations and mass fluxes in each compartment. The second core script is Solve_bovine_puffV04_integration.m, which couples the output of the control volume model to a puff-based fluid dynamics model. This code initializes the puff using integrated physiological parameters and solves a system of ordinary differential equations to predict the spatiotemporal evolution of methane concentration and velocity fields. Two supporting functions are included: 

PuffSolver.m: Solves the puff trajectory and concentration field using Gaussian self-similar profiles and conservation laws for momentum, buoyancy, and scalar transport.
BelchProfileGenerator.m: Generates a lognormal flow profile representing the temporal shape of a belching event, which serves as input to the control volume model.

All files are written in MATLAB and are compatible with versions R2021a and later. Users can modify physiological parameters, environmental conditions, and sensor configurations to simulate different scenarios. The software is intended to support researchers working on methane sensing, livestock emissions modeling, and sensor calibration.

To run the full simulation:
Start with MassConservationV05.m to simulate the control volume system.
Then run Solve_bovine_puffV04_integration.m to compute the puff evolution and sensor capture fraction.
Use the supporting functions as needed to customize the emission profile and puff behavior.
This software supports the reproducibility and extension of the results presented in the associated manuscript and is intended for open scientific use.
DOI: 10.17632/ttyht4bypb.1
title: From Concentration to Flux: A Mechanistic Model for Accurate Methane Emission Estimates in Livestock

description: This dataset contains MATLAB source code used to implement the hybrid multiphysics framework described in the manuscript titled "A Physics-Based Framework for Accurate On-Farm Enteric Methane Sensing." The software models the complete emission-to-sensor pathway for methane released during cattle eructation events, integrating physiological control volume analysis with a transient buoyant puff model.

The main script is MassConservationV05.m, which simulates methane transport through four interconnected control volumes representing the rumen, lungs, local environment, and sampling device. It applies the Reynolds Transport Theorem to derive time-resolved methane concentrations and mass fluxes in each compartment. The second core script is Solve_bovine_puffV04_integration.m, which couples the output of the control volume model to a puff-based fluid dynamics model. This code initializes the puff using integrated physiological parameters and solves a system of ordinary differential equations to predict the spatiotemporal evolution of methane concentration and velocity fields. Two supporting functions are included: 

PuffSolver.m: Solves the puff trajectory and concentration field using Gaussian self-similar profiles and conservation laws for momentum, buoyancy, and scalar transport.
BelchProfileGenerator.m: Generates a lognormal flow profile representing the temporal shape of a belching event, which serves as input to the control volume model.

All files are written in MATLAB and are compatible with versions R2021a and later. Users can modify physiological parameters, environmental conditions, and sensor configurations to simulate different scenarios. The software is intended to support researchers working on methane sensing, livestock emissions modeling, and sensor calibration.

To run the full simulation:
Start with MassConservationV05.m to simulate the control volume system.
Then run Solve_bovine_puffV04_integration.m to compute the puff evolution and sensor capture fraction.
Use the supporting functions as needed to customize the emission profile and puff behavior.
This software supports the reproducibility and extension of the results presented in the associated manuscript and is intended for open scientific use.
DOI: 10.17632/vy98vyf8n2
title: Biopolyolysis - a new biopathway for recycling waste polyurethane foams

description: The research described in this article concerns eco-design of thermal insulation polyurethane biomaterials in accordance with the assumptions of the European Green Deal. Biofoams based on three different biopolyols derived from rapeseed oil were obtained. In the foams, 100% of the petrochemical polyol was replaced with a selected biopolyol. The biopolyols were obtained in the reactions of transesterification of rapeseed oil with triethanolamine, transesterification with diethylene glycol and in the reaction of epoxidation and oxirane ring opening with diethylene glycol. The material obtained from the petrochemical polyol was used as a reference foam. The foams were subjected to polyolysis using the biopolyol obtained in the reaction of transesterification of rapeseed oil with triethanolamine. Chemolysis of the same foams using diethylene glycol played the role of a comparative reaction. The obtained rebiopolyols were characterized by hydroxyl numbers comparable to those of the biopolyols and significantly lower than those of typical recyclates obtained in the glycolysis reaction. This type of research has not yet been described in the literature. The new biocomponents obtained as a result of the polyolysis reaction were used to produce open-cell foams in which 100% of the petrochemical polyol was replaced with a rebiopolyol. The resulting foam materials were characterized by a thermal conductivity coefficient of about 36 mW/m·K, density of about 15 kg/m3 and compressive strength of about 50 kPa.
DOI: 10.17632/vy98vyf8n2.1
title: Biopolyolysis - a new biopathway for recycling waste polyurethane foams

description: The research described in this article concerns eco-design of thermal insulation polyurethane biomaterials in accordance with the assumptions of the European Green Deal. Biofoams based on three different biopolyols derived from rapeseed oil were obtained. In the foams, 100% of the petrochemical polyol was replaced with a selected biopolyol. The biopolyols were obtained in the reactions of transesterification of rapeseed oil with triethanolamine, transesterification with diethylene glycol and in the reaction of epoxidation and oxirane ring opening with diethylene glycol. The material obtained from the petrochemical polyol was used as a reference foam. The foams were subjected to polyolysis using the biopolyol obtained in the reaction of transesterification of rapeseed oil with triethanolamine. Chemolysis of the same foams using diethylene glycol played the role of a comparative reaction. The obtained rebiopolyols were characterized by hydroxyl numbers comparable to those of the biopolyols and significantly lower than those of typical recyclates obtained in the glycolysis reaction. This type of research has not yet been described in the literature. The new biocomponents obtained as a result of the polyolysis reaction were used to produce open-cell foams in which 100% of the petrochemical polyol was replaced with a rebiopolyol. The resulting foam materials were characterized by a thermal conductivity coefficient of about 36 mW/m·K, density of about 15 kg/m3 and compressive strength of about 50 kPa.
DOI: 10.17632/fzp9xknftp
title: The effects of potting substrate compositions on the growth of potted hellebore (Helleborus × hybridus)

description: The potted hellebore is widely favored in horticulture owing to its diverse flower colors and special flower shapes. As a potted plant with remarkable ornamental value, it enjoys promising development prospects in the ornamental plant market. However, scientific literature remains scarce regarding the effects of different composite substrates on the growth of Helleborus×hybridus, the focus of this study. Given its distinctive aesthetic value and the crucial significance of the substrate for its growth, this study examined the growth status of potted seedlings in various substrate compositions. Key growth and photosynthetic parameters were measured, and a comprehensive evaluation was carried out using principal component analysis (PCA). Results demonstrated that the substrate containing imported peat mixed with vermiculite (1:1 ratio) yielded optimal plant growth, followed by coconut coir combined with vermiculite (1:1). These findings provide practical insights for selecting environmentally sustainable and cost-effective substrate components for hellebores cultivation. We believe that the present paper will be of interest to the readership of your journal.