Data, Volume 7, Issue 12 (December 2022) – 16 articles

This article provides detailed redox data on nine differently substituted benzophenones and ten square planar copper(II) complexes containing 2-hydroxyphenones obtained by cyclic voltammetry (CV) experiments. The information provided is related to the published full research articles “An electrochemical and computational chemistry study of substituted benzophenones” (Electrochim. Acta2021, 373, 137894) and “Electrochemical behaviour of copper(II) complexes containing 2-hydroxyphenones” (Electrochim. Acta2022, 424, 140629), where the CVs and electrochemical data at mainly one scan rate, namely at 0.100 Vs⁻¹, are reported. CVs and the related peak current and voltage values, not reported in the related research article, are provided in this article for nine differently substituted benzophenones and ten differently substituted copper-2-hydroxyphenone complexes at various scan rates over more than two orders of magnitude. The redox data presented are the first reported complete set of electrochemical data of nine 2-hydroxyphenones and ten copper(II) complexes containing 2-hydroxyphenone ligands. Full article

Medical errors are recognized as major threats to patient safety worldwide. Lack of streamlined communication and an inability to share and exchange data are among the contributory factors affecting patient safety. To address these challenges, blockchain can be utilized to ensure a secure, transparent and decentralized data exchange among stakeholders. In this study, we discuss six use cases that can benefit from blockchain to gain operational effectiveness and efficiency in the patient safety context. The role of stakeholders, system requirements, opportunities and challenges are discussed in each use case in detail. Connecting stakeholders and data in complex healthcare systems, blockchain has the potential to provide an accountable and collaborative milieu for the delivery of safe care. By reviewing the potential of blockchain in six use cases, we suggest that blockchain provides several benefits, such as an immutable and transparent structure and decentralized architecture, which may help transform health care and enhance patient safety. While blockchain offers remarkable opportunities, it also presents open challenges in the form of trust, privacy, scalability and governance. Future research may benefit from including additional use cases and developing smart contracts to present a more comprehensive view on potential contributions and challenges to explore the feasibility of blockchain-based solutions in the patient safety context. Full article

One of the key functions of driver monitoring systems is the evaluation of the driver’s state, which is a key factor in improving driving safety. Currently, such systems heavily rely on the technology of deep learning, that in turn requires corresponding high-quality datasets to achieve the required level of accuracy. In this paper, we introduce a dataset that includes information about the driver’s state synchronized with the vehicle telemetry data. The dataset contains more than 17.56 million entries obtained from 633 drivers with the following data: the driver drowsiness and distraction states, smartphone-measured vehicle speed and acceleration, data from magnetometer and gyroscope sensors, g-force, lighting level, and smartphone battery level. The proposed dataset can be used for analyzing driver behavior and detecting aggressive driving styles, which can help to reduce accidents and increase safety on the roads. In addition, we applied the K-means clustering algorithm based on the 11 least-correlated features to label the data. The elbow method showed that the optimal number of clusters could be either two or three clusters. We chose to proceed with the three clusters to label the data into three main scenarios: parking and starting driving, driving in the city, and driving on highways. The result of the clustering was then analyzed to see what the most frequent critical actions inside the cabin in each scenario were. According to our analysis, an unfastened seat belt was the most frequent critical case in driving in the city scenario, while drowsiness was more frequent when driving on the highway. Full article

Tyumen City is a large city in Western Siberia. This territory has ecological problems, which are typical to many cities around the world, including the loss of biodiversity and environment, habitat pollution, and others. This data paper presents for the first time the plant species composition of 11 natural forest and floodplain areas in Tyumen City. In a city, forests provide a refuge for both threatened plants and weeds (including alien species). In these ecosystems, unique communities are being formed, where both threatened and alien plants can co-occur. Within the city’s area, forests serve as separate green “islands” among urbanized landscapes. A total of 11 forest and floodplain areas have been studied based on field surveys conducted by the authors of the paper in 2020–2022. The obtained data (8742 observations representing 434 species, accepted subspecies, and hybrids belonging to 270 genera and 74 families) serve as a basis for the modern flora of Tyumen City, its conservation, and counteraction to the introduction of alien plants. Full article

This paper describes a benchmark dataset for the detection of fuel particles in 2D and 3D image data in a rotary kiln combustion chamber. The specific challenges of detecting the small particles under demanding environmental conditions allows for the performance of existing and new particle detection techniques to be evaluated. The data set includes a classification of burning and non-burning particles, which can be in the air but also on the rotary kiln wall. The light-field camera used for data generation offers the potential to develop and objectively evaluate new advanced particle detection methods due to the additional 3D information. Besides explanations of the data set and the contained ground truth, an evaluation procedure of the particle detection based on the ground truth and results for an own particle detection procedure for the data set are presented. Full article

Clinical data analysis could lead to breakthroughs. However, clinical data contain sensitive information about participants that could be utilized for unethical activities, such as blackmailing, identity theft, mass surveillance, or social engineering. Data anonymization is a standard step during data collection, before sharing, to overcome the risk of disclosure. However, conventional data anonymization techniques are not foolproof and also hinder the opportunity for personalized evaluations. Much research has been done for synthetic data generation using generative adversarial networks and many other machine learning methods; however, these methods are either not free to use or are limited in capacity. This study evaluates the performance of an emerging tool named synthpop, an R package producing synthetic data as an alternative approach for data anonymization. This paper establishes data standards derived from the original data set based on the utilities and quality of information and measures variations in the synthetic data set to evaluate the performance of the data synthesis process. The methods to assess the utility of the synthetic data set can be broadly divided into two approaches: general utility and specific utility. General utility assesses whether synthetic data have overall similarities in the statistical properties and multivariate relationships with the original data set. Simultaneously, the specific utility assesses the similarity of a fitted model’s performance on the synthetic data to its performance on the original data. The quality of information is assessed by comparing variations in entropy bits and mutual information to response variables within the original and synthetic data sets. The study reveals that synthetic data succeeded at all utility tests with a statistically non-significant difference and not only preserved the utilities but also preserved the complexity of the original data set according to the data standard established in this study. Therefore, synthpop fulfills all the necessities and unfolds a wide range of opportunities for the research community, including easy data sharing and information protection. Full article

Mobile health (mHealth) is playing a key role in facilitating health services for patients. Such services may include remote diagnostics and monitoring, chronic conditions management, preventive medicine, and health promotion. While mHealth has gained significant traction during the COVID-19 pandemic, they may pose safety risks to patients. This entails regulations and monitoring of shared data and management of potential safety risks of all mHealth applications continuously and systematically. In this study, we propose a blockchain-based framework for regulating mHealth apps and governing their safe use. We systematically identify the needs, stakeholders, and requirements of the current mHealth practices and regulations that may benefit from blockchain features. Further, we exemplify our framework on a diabetes mHealth app that supports safety risk assessment and incident reporting functions. Blockchain technology can offer a solution to achieve this goal by providing improved security, transparency, accountability, and traceability of data among stakeholders. Blockchain has the potential to alleviate existing mHealth problems related to data centralization, poor data quality, lack of trust, and the absence of robust governance. In the paper, we present a discussion on the security aspects of our proposed blockchain-based framework, including limitations and challenges. Full article

The more complex and globalized social structures become, the greater the need for new ways of exchanging information and knowledge. Legal science is a field that needs to be codified to allow the interoperability between people and states, as well as between humans and machines. The objective of this work is to develop an ontology in order to describe two different pillars of codified law (civil and criminal) and be able to depict the interaction between them. To answer the above question, we examine the Greek Criminal Law as depicted in the Greek Penal Code (ΠΚ) and the way its articles can be analyzed. Then we examine Tort as described in the Greek Civil Code (AΚ) and link the two codifications through the concepts of illegality and damage, both being prerequisites of tortious liability. Following that, through the Protégé application, a legal ontology is created in the OWL semantic language, while finally, four articles of the Penal Code are codified in the ontology and a presentation of their relation to the civil tort is required from a reasoning algorithm. Full article

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning. Full article

Evaporation from soils is critical for agricultural water management. This requires a clear understanding of the water retention and soil shrinkage behavior of soils during water escape and due to fertilizers usage. Based on laboratory testing, this paper provides a comprehensive dataset generated for the determination of the geotechnical properties of inert silty sand and active lean clay using distilled water and saline pore fluid under ambient conditions. The tests include fluid-independent general soil properties, fluid-dependent specific soil properties, low-demand evaporation as a baseline, and high-demand evaporation to capture summer. Full article

The digital twin has recently become a popular topic in research related to manufacturing, such as Industry 4.0, the industrial internet of things, and cyber-physical systems. In addition, digital twins are the focus of several research areas: construction, urban management, digital transformation of the economy, medicine, virtual reality, software testing, and others. The concept is not yet fully defined, its scope seems unlimited, and the topic is relatively new; all this can present a barrier to research. The main goal of this paper is to develop a proper methodology for visualizing the digital-twin science landscape using modern bibliometric tools, text-mining and topic-modeling, based on machine learning models—Latent Dirichlet Allocation (LDA) and BERTopic (Bidirectional Encoder Representations from Transformers). The scope of the study includes 8693 publications on the topic selected from the Scopus database, published between January 1993 and September 2022. Keyword co-occurrence analysis and topic-modeling indicate that studies on digital twins are still in the early stage of development. At the same time, the core of the topic is growing, and some topic clusters are emerging. More than 100 topics can be identified; the most popular and fastest-growing topic is ‘digital twins of industrial robots, production lines and objects.’ Further efforts are needed to verify the proposed methodology, which can be achieved by analyzing other research fields. Full article

This article analyses primary data on the entrepreneurship intentions of selected Zimbabwean college students. The goal of this study was to examine the measurement invariance of the entrepreneurship goal and implementation intention scales across gender groups in a higher education setting. Entrepreneurship goal intentions (EGI) and entrepreneurship implementation intentions (EII) are examined as separate but related constructs. To address the research goal, a positivist philosophy and quantitative research approach were used. A cross-sectional survey was used to collect data from a convenient sample of 262 college students in Zimbabwe. A researcher-administered questionnaire, written in English, was distributed to the respondents and collected after completion. Multi-group confirmatory analysis was performed on the dataset using JASP computer software. The results obtained confirmed all four levels of measurement invariance, namely configural, metric, scalar, and strict invariance. The pattern of the results validates the consistency of the measurement properties of the entrepreneurial intention instruments designed in developed countries across different contexts of use. Researchers, entrepreneurship educators, and policymakers in Zimbabwe can use the results of this analysis to quantify potential entrepreneurs among young adults and to come up with intervention measures to support future entrepreneurship. Full article

This article presents experimental data and nonlinear finite element analysis (NLFEA) modeling for an innovative buckling restrained bracing (BRB) system. The data were collected from qualification testing of introduced BRBs per the AISC 341 test provision and finite element modeling. The BRB is made of three parts: core bar, restraining unit, and end units, in which duplicates of three different core bar cross sections (i.e., fully threaded, threaded notched, and smooth shaved) were tested. The BRBs introduced in this research come with innovative end parts, so-called fingers. These fingers provide the longitudinal gap required in every BRB system and simultaneously prevent buckling of the core bar at the end regions at both ends of the BRB sample, thus facilitating an easy core replacement if it gets damaged in the event of an earthquake. The measured parameters were the applied cyclic load and the corresponding displacement. Analysis of the acquired data illustrated an almost symmetric hysteric behavior with a little higher capacity under compression but a noticeable overall ductility of 4. Moreover, finite element modeling data for one type of core bar (fully threaded) were curated. The data presented in this paper will be valuable for fabricating BRBs in practice and further research on the topic considered. Full article

With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes. Full article

The Montefeltro seismic network (FDSN Network code: 1S) was deployed in the Apennines area of northern Marche and southern Emilia-Romagna regions (central Italy). A temporary network was set up in December 2018 and continues to operate, with an array consisting of stations equipped with dynamic digitizers and three-component short/extended/broad band seismometers (Guralp CMG/20s and 30s, Lennartz 3D/5s, Sara SS20 3D/0.5s sensors). The network records in continuous mode at 100 sps. The data are used to analyze the seismic activity and the spatiotemporal evolution of small seismic sequences occurring in the considered area and surrounding zones, strongly clustered in time and space. The data of dataset files are mini-seed formatted and subdivided by the following tree: (1) the dataset is divided by years; (2) the dataset is then subdivided by stations; (3) finally, the data are divided by days of each year in every station folder. Full article

Automated spectrum analysis serves as a troubleshooting tool that helps to diagnose faults in wireless networks such as difficult signal propagation conditions and coexisting wireless networks. It provides a higher monitoring coverage while requiring less expertise compared with manual spectrum analysis. In this paper, we introduce a data set that can be used to train and evaluate deep learning models, capable of detecting frames from different wireless standards as well as interference between single frames. Since manually labeling a high variety of frames in different environments is too challenging, an artificial data generation pipeline was developed. The data set consists of 20,000 augmented signal segments, each containing a random number of different Wi-Fi and Bluetooth frames, their spectral image representations and labels that describe the position and type of frame within the spectrogram. The data set contains results of intermediate processing steps that enable the research or teaching community to create new data sets for specific requirements or to provide new interesting examination examples. Full article