cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/28557767
title: Operations Research Meets Machine Learning: Bayesian Optimization for Accelerating the Product Development in Additive Manufacturing and Thermoelectric Materials

description: The design of chemical-based products and functional materials is vital to modern technologies, yet remains challenging due to costly and time-consuming manufacturing processes. These processes often involve tens of variables, resulting in hundreds of thousands of possible experiments, with each experiment carrying significant resource costs. In such high-dimensional domains, traditional Edisonian, trial-and-error approaches become prohibitively expensive and inefficient at identifying optimal experimental conditions. Consequently, there is a critical need to shift from these conventional methods to more systematic, data-driven decision-making.

Machine learning (ML) and operations research (OR) offer promising approaches to address these challenges through novel optimization frameworks. By building surrogate models that capture the relationships between decision variables and targeted objectives, ML enables predictive modeling of complex manufacturing processes. OR then integrates these pre-trained models into a unified optimization framework, facilitating data-driven, rational, and scientifically grounded decisions that accelerate product development while minimizing experimental costs.

In this work, we present an ML- and OR-based framework that combines Bayesian optimization (BO) with first-principles knowledge. We demonstrate its effectiveness in solving industrial manufacturing optimization problems across additive manufacturing and thermoelectric material domains, including flash sintering, plasma sintering, and aerosol jet printing. Our framework accelerates the identification of optimal experimental conditions while reducing both economic and labor costs. Moreover, by incorporating physical knowledge in multiple ways, it is ideally suited for data-scarce, customized, and expensive experiments. It is a general framework that is not restricted to any single application domain.
DOI: 10.7274/28557767.v1
title: Operations Research Meets Machine Learning: Bayesian Optimization for Accelerating the Product Development in Additive Manufacturing and Thermoelectric Materials

description: The design of chemical-based products and functional materials is vital to modern technologies, yet remains challenging due to costly and time-consuming manufacturing processes. These processes often involve tens of variables, resulting in hundreds of thousands of possible experiments, with each experiment carrying significant resource costs. In such high-dimensional domains, traditional Edisonian, trial-and-error approaches become prohibitively expensive and inefficient at identifying optimal experimental conditions. Consequently, there is a critical need to shift from these conventional methods to more systematic, data-driven decision-making.

Machine learning (ML) and operations research (OR) offer promising approaches to address these challenges through novel optimization frameworks. By building surrogate models that capture the relationships between decision variables and targeted objectives, ML enables predictive modeling of complex manufacturing processes. OR then integrates these pre-trained models into a unified optimization framework, facilitating data-driven, rational, and scientifically grounded decisions that accelerate product development while minimizing experimental costs.

In this work, we present an ML- and OR-based framework that combines Bayesian optimization (BO) with first-principles knowledge. We demonstrate its effectiveness in solving industrial manufacturing optimization problems across additive manufacturing and thermoelectric material domains, including flash sintering, plasma sintering, and aerosol jet printing. Our framework accelerates the identification of optimal experimental conditions while reducing both economic and labor costs. Moreover, by incorporating physical knowledge in multiple ways, it is ideally suited for data-scarce, customized, and expensive experiments. It is a general framework that is not restricted to any single application domain.
DOI: 10.17632/8s9xj3x58s.1
title: Dataset of original studies that reported data on pre-hospital non-medical help provided for snakebites

description: This dataset contains the results of analysis of original peer-reviewed articles in English published between 2004 and 2024 that reported quantitative data on the real-life application of pre-hospital non-medical help in snakebite incidents.
The search of eligible papers was carried out in October 2024 in PubMed using the following request: ((snakebit*[Title/Abstract]) OR (snake* bit*[Title/Abstract]) OR (snake* envenom*[Title/Abstract]) OR (snake* venom*[Title/Abstract]) OR (Snake Bites[MeSH Terms])) AND (2004:2024[pdat]) AND (English[Language]).
The dataset table collected the following data (sheet #1 "Dataset"): article author(s), year of publication, article source link, study design, study geography (country), study sample characteristics, sample size, types of providers of pre-hospital non-medical help, types of help-seeking and helping behaviours and practices, rates of their occurrence presented as percentages with one decimal place, and data on statistically significant associations between particular helping practices and health outcomes.
Sheet #2 ("Bibliography") of the dataset file contains a complete bibliography of the articles included in the analysis (n=158).
The dataset was utilised to perform the related scoping review.
DOI: 10.17632/yzmgkcjh86.1
title: Dataset and Code for Protecting communication and computing systems against poisoning attacks for Cloud Edge-Based Federated Learning System

description: Because of its significance and extensive usage, the KDDCUP'99 dataset is used in our edge-based intrusion detection study. The abundance of attack types and scenarios included, together with the extensive feature set and large instance repository, make this dataset an invaluable addition to the discipline. When comparing and contrasting different intrusion detection systems and approaches, this statistic is essential. The following investigation will shed light on the difficulties and restrictions imposed by the KDDCUP'99 dataset. These difficulties are exacerbated by the following factors: the age of the underlying network environment, the existence of unnecessary and uninformative characteristics, the prevalence of mislabelled instances, and the inherent bias in class distribution
DOI: 10.17632/yzmgkcjh86.2
title: Dataset and Code for Protecting communication and computing systems against poisoning attacks for Cloud Edge-Based Federated Learning System

description: Because of its significance and extensive usage, the KDDCUP'99 dataset is used in our edge-based intrusion detection study. The abundance of attack types and scenarios included, together with the extensive feature set and large instance repository, make this dataset an invaluable addition to the discipline. When comparing and contrasting different intrusion detection systems and approaches, this statistic is essential. The following investigation will shed light on the difficulties and restrictions imposed by the KDDCUP'99 dataset. These difficulties are exacerbated by the following factors: the age of the underlying network environment, the existence of unnecessary and uninformative characteristics, the prevalence of mislabelled instances, and the inherent bias in class distribution
DOI: 10.15468/dl.v99njj
title: Occurrence Download

description: A dataset containing 2259 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "TaxonKey is Meles meles (Linnaeus, 1758)"
 ]
}

 The dataset includes 2259 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015160-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/3pgt34xkfv.1
title: Rose Image Classification Dataset

description: The Rose Image Classification dataset contains 445 high-quality images of roses, categorized by color. This dataset is ideal for machine learning tasks involving image classification, recognition, and other computer vision applications. Each image is labeled according to the color of the rose, allowing for the development of models that can accurately classify rose varieties based on visual features.

Number of Images: 445
Color:
Red Roses: 201 images
Yellow Roses: 58 images
White Roses: 85 images
Pink Roses: 93 images
DOI: 10.17632/nwc6fyb5xx.1
title: data Blockchain project

description: This research on governance challenges used qualitative data collected through 10 semi-structured interviews with experts, including administrative officials, policymakers, and researchers. Conducted between December 2023 and November 2024, the interviews addressed themes such as transparency, participatory decision-making, and resource management, guided by a framework based on existing literature. Interviews were conducted in person or online, recorded with participant consent, and analyzed using NVivo software. Ethical considerations included signed consent forms ensuring confidentiality and institutional ethics committee approval. This approach yielded rich insights into governance challenges.
DOI: 10.17632/nwc6fyb5xx.2
title: data Blockchain project

description: This research on governance challenges used qualitative data collected through 12 semi-structured interviews with experts, including administrative officials, policymakers, and researchers. Conducted between December 2023 and November 2024, the interviews addressed themes such as transparency, participatory decision-making, and resource management, guided by a framework based on existing literature. Interviews were conducted in person or online, recorded with participant consent, and analyzed using NVivo software. Ethical considerations included signed consent forms ensuring confidentiality and institutional ethics committee approval. This approach yielded rich insights into governance challenges.
DOI: 10.17632/nwc6fyb5xx.3
title: data Blockchain project

description: This research on governance challenges employed qualitative data collected through 10 semi-structured interviews with experts who were either directly involved in or indirectly connected to the TradeLens project. The participants included administrative officials, stakeholders, and researchers possessing firsthand knowledge or significant insight into the governance dynamics of TradeLens. The interviews explored key themes such as transparency, participatory decision-making, and resource management, following a framework grounded in the existing literature.
All interviews were conducted either in person or via online platforms, and explicit consent was obtained from each participant prior to recording. Participants were informed about the purpose of the study, the voluntary nature of their involvement, and confidentiality measures in place. The collected data were analyzed using NVivo software to ensure systematic coding and thematic analysis.
Ethical considerations were rigorously observed, with approval granted by the institutional ethics committee, ensuring that the study adhered to ethical standards and safeguarded participant confidentiality. This methodological approach provided deep and nuanced insights into the governance challenges encountered within the TradeLens ecosystem.
DOI: 10.17632/xrjw2wrwgr.1
title: Rolled AZ31 - Kearns factor Analysis

description: Experimental work for a rolled AZ31 magnesium. Experiments are for RD and TD tension, and RD, TD, and ND compression. EBSD images taken of compressed specimen throughout loading.
DOI: 10.7274/28586684
title: DESIGN OF AMINE-BASED CATALYTIC POLYMERIC MEMBRANES THROUGH STRUCTURAL MODIFICATION FOR INTEGRATED CO2 CAPTURE AND CONVERSION

description: Amine-based catalysts hold significant promise for CO2 conversion into value-added cyclic carbonates due to their 100% atom economy, low energy demands, cost efficiency, and eco-friendly nature. However, their broader application under mild conditions is hindered by a limited understanding of structure-performance relationships. This work investigates the structural modification of poly(4-vinylpyridine) (P4VP) through quaternization with alkyl-halides to enhance catalytic performance and recovery. Catalysts were tested in a batch reactor with epichlorohydrin (ECH) and CO2 at mild temperatures (57°C), producing chloroprene carbonate. Results show that catalytic performance improves with increasing alkyl chain length due to enhanced solubility in ECH and weakened pyridinium-halide interactions — though longer chains reduced catalyst recovery. Counterion strength also played a pivotal role: iodine (I), with lower electronegativity, outperformed bromine (Br) by increasing catalytic availability and minimizing water interactions, enhancing hydrogen bond donor (HBD) effectiveness. This foundational work supported further functionalization efforts.

The second part investigates P4VP-metal complexes for CO2 conversion, aiming to enhance catalytic performance and recovery. Catalysts were synthesized and evaluated under identical conditions. Results highlighted that performance depended on metal type, pyridine structure, counterion, and pyridine-to-metal ratio. A synergistic effect was observed uniquely with cobalt complexes, with P4VP-CoCl2 showing twice the production rate of P4VP-CoCO3. The pyridine structure’s strong interaction with cobalt contributed to this synergy, while an ideal P4VP-to-CoCl2 ratio of 4:1 maximized catalytic efficiency. These findings provide crucial insights into structural modifications and counterion selection, laying the groundwork for next-generation, high-performance amine-based catalysts with enhanced functionalities for complex CO2 conversion reactions.
DOI: 10.7274/28586684.v1
title: DESIGN OF AMINE-BASED CATALYTIC POLYMERIC MEMBRANES THROUGH STRUCTURAL MODIFICATION FOR INTEGRATED CO2 CAPTURE AND CONVERSION

description: Amine-based catalysts hold significant promise for CO2 conversion into value-added cyclic carbonates due to their 100% atom economy, low energy demands, cost efficiency, and eco-friendly nature. However, their broader application under mild conditions is hindered by a limited understanding of structure-performance relationships. This work investigates the structural modification of poly(4-vinylpyridine) (P4VP) through quaternization with alkyl-halides to enhance catalytic performance and recovery. Catalysts were tested in a batch reactor with epichlorohydrin (ECH) and CO2 at mild temperatures (57°C), producing chloroprene carbonate. Results show that catalytic performance improves with increasing alkyl chain length due to enhanced solubility in ECH and weakened pyridinium-halide interactions — though longer chains reduced catalyst recovery. Counterion strength also played a pivotal role: iodine (I), with lower electronegativity, outperformed bromine (Br) by increasing catalytic availability and minimizing water interactions, enhancing hydrogen bond donor (HBD) effectiveness. This foundational work supported further functionalization efforts.

The second part investigates P4VP-metal complexes for CO2 conversion, aiming to enhance catalytic performance and recovery. Catalysts were synthesized and evaluated under identical conditions. Results highlighted that performance depended on metal type, pyridine structure, counterion, and pyridine-to-metal ratio. A synergistic effect was observed uniquely with cobalt complexes, with P4VP-CoCl2 showing twice the production rate of P4VP-CoCO3. The pyridine structure’s strong interaction with cobalt contributed to this synergy, while an ideal P4VP-to-CoCl2 ratio of 4:1 maximized catalytic efficiency. These findings provide crucial insights into structural modifications and counterion selection, laying the groundwork for next-generation, high-performance amine-based catalysts with enhanced functionalities for complex CO2 conversion reactions.
DOI: 10.15468/dl.cy5p37
title: Occurrence Download

description: A dataset containing 223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Periplaneta japonica Karny, 1908",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 223 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015161-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/4vkgjh95b8.1
title: Heavy metals and microplastics in Mytilus galloprovincialis and Xenostrobus securis from Asturias, SW Bay of Biscay 2.0

description: Data of lenght, weight, heavy metal content and microplastics counts from Mytilus galloprovincialis and Xenostrobus securis. Version 2.0.
DOI: 10.15468/dl.h8mgjg
title: Occurrence Download

description: A dataset containing 1676 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Melanostomias bartonbeani Parr, 1927, Melanostomias biseriatus Regan & Trewavas, 1930, Melanostomias globulifer Fowler, 1934, Melanostomias macrophotus Regan & Trewavas, 1930, Melanostomias margaritifer Regan & Trewavas, 1930, Melanostomias melanopogon Regan & Trewavas, 1930, Melanostomias melanops Brauer, 1902, Melanostomias niger Gilchrist & von Bonde, 1924, Melanostomias nigroaxialis Parin & Pokhil'skaya, 1978, Melanostomias paucilaternatus Parin & Pokhil'skaya, 1978, Melanostomias pauciradius Matsubara, 1938, Melanostomias pollicifer Parin & Pokhil'skaya, 1978, Melanostomias stewarti Fowler, 1934, Melanostomias tentaculatus (Regan & Trewavas, 1930), Melanostomias valdiviae Brauer, 1902, Melanostomias vierecki Fowler, 1934)"
 ]
}

 The dataset includes 1676 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015162-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/w6wnxj42b9.1
title: Establishing normative values of seated medicine ball throw test among college going athletes

description: Seated medicine ball throw test will help to determine the upper limb strength and power among various overhead activities related sports such as badminton, basketball and squash in which normative values have been not established. We collected information from a group of athletes of all genders and sports, ages 18 to 24. After following strict inclusion and exclusion criteria and assessing the covariates and demographic characteristics, participants were included. Every participant performed the SMBT in a controlled environment and we recorded their average of three throws. The importance of training experience, the links between certain sports and distance thrown by athletes, and the variations in performance across genders and different sports are some important insights were learned. With this information, coaches, strength coaches and sports scientists may tailor their analysis and development of athletes.
DOI: 10.17632/h2y5v27ncw.1
title: Translating Validating and Establishing Reliability of Foot Function Index Scale in Population with Plantar Fasciitis

description: The Foot Function Index (FFI) is a self-reported questionnaire used to assess foot pain, disability, and functional limitations in individuals with plantar fasciitis. It consists of 23 items divided into three domains: Pain, Disability, and Functional Limitation. Since Hindi is widely spoken in India, translating, validating, and assessing the reliability of the FFI in Hindi is essential. A validated and reliable Hindi version will help individuals accurately report their symptoms, leading to better diagnosis and treatment. This will enhance patient care and improve the management of plantar fasciitis in Hindi-speaking populations.
DOI: 10.7274/28557770
title: A Radio Frequency Polarimetry Sensor for Aircraft Engine Health Monitoring

description: RF sensors are a non-contact technology that have the potential to provide on-wing aircraft engine health information. The RF sensor response to rotor whirl and compressor blade vibration was investigated through a series of experiments. Data processing methods involving neural networks were used to separate the desired RF response from the unwanted response.

The RF sensor response to whirl was experimentally investigated through static offset and rotor whirl tests. Static offset experiments showed a one-to-one invertible mapping between the rotor shaft location and the RF polarimetry data. A simple, multi-layer neural network was successfully used to perform a regression analysis to find this mapping. This work also showed that the RF sensors are sensitive to rotor whirl shape and size. Since the rotor shaft traces an ellipse once per revolution during rotor whirl, whirl was anticipated to be directly related to a once-per-revolution response in the RF signal. The RF signal was pre-conditioned by computing the variance of the RF signal at shaft rotational frequency. This pre-conditioned data was fed into a simple, multi-layer neural network. The neural network was able to predict the whirl major and minor axes lengths. 

The RF sensor response to rotor rotation and blade vibration was experimentally investigated. The experiments showed that the RF sensors are sensitive to blade vibration for synchronous and asynchronous vibrations. A model of amplitude and frequency modulation was formulated in order to describe the RF sensor response to rotor rotation and blade vibration. It follows from this model that amplitude and frequency modulation causes a large number of asymmetrical sidebands to appear around the RF tone, and that the blade vibration response is in these sidebands. The RF results were consistent with this model. Data from the RF sensors were pre-conditioned based on the model of amplitude and frequency modulation and used as an input into a simple, multi-layer neural network. The neural network regression algorithm was successfully able to predict the blade vibration deflection by nodal diameter.