Data, Volume 9, Issue 2 (February 2024) – 21 articles

Wars, conflicts, and peace efforts have become inherent characteristics of regions, and understanding the prevailing sentiments related to these issues is crucial for finding long-lasting solutions. Twitter/‘X’, with its vast user base and real-time nature, provides a valuable source to assess the raw emotions and opinions of people regarding war, conflict, and peace. This paper focuses on collecting and curating hinglish tweets specifically related to wars, conflicts, and associated taxonomy. The creation of said dataset addresses the existing gap in contemporary literature, which lacks comprehensive datasets capturing the emotions and sentiments expressed by individuals regarding wars, conflicts, and peace efforts. This dataset holds significant value and application in deep pragmatic analysis as it enables future researchers to identify the flow of sentiments, analyze the information architecture surrounding war, conflict, and peace effects, and delve into the associated psychology in this context. To ensure the dataset’s quality and relevance, a meticulous selection process was employed, resulting in the inclusion of explanable 500 carefully chosen search filters. The dataset currently has 10,040 tweets that have been validated with the help of human expert to make sure they are correct and accurate. Full article

Coastal dunes worldwide are increasingly under pressure from the adverse effects of human activities. Therefore, more and more restoration measures are being taken to create conditions that help disturbed coastal dune ecosystems regenerate or recover naturally. However, many projects lack the (open-access) monitoring observations needed to signal whether further actions are needed, and hence lack the opportunity to “learn by doing”. This submission presents an open-access data set of 37 high-resolution digital elevation models and 24 orthomosaics collected before and after the excavation of five artificial foredune trough blowouts (“notches”) in winter 2012/2013 in the Dutch Zuid-Kennemerland National Park, one of the largest coastal dune restoration projects in northwest Europe. These high-resolution data provide a valuable resource for improving understanding of the biogeomorphic processes that determine the evolution of restored dune systems as well as developing guidelines to better design future restoration efforts with foredune notching. Full article

As one of the most important topics in contemporary computer vision research, object detection has received wide attention from the precision agriculture community for diverse applications. While state-of-the-art object detection frameworks are usually evaluated against large-scale public datasets containing mostly non-agricultural objects, a specialized dataset that reflects unique properties of plants would aid researchers in investigating the utility of newly developed object detectors within agricultural contexts. This article presents AriAplBud: a close-up apple flower bud image dataset created using an unmanned aerial vehicle (UAV)-based red–green–blue (RGB) camera. AriAplBud contains 3600 images of apple flower buds at six growth stages, with 110,467 manual bounding box annotations as positive samples and 2520 additional empty orchard images containing no apple flower bud as negative samples. AriAplBud can be directly deployed for developing object detection models that accept Darknet annotation format without additional preprocessing steps, serving as a potential benchmark for future agricultural object detection research. A demonstration of developing YOLOv8-based apple flower bud detectors is also presented in this article. Full article

Previous studies from the Netherlands, Germany, and Argentina revealed that the 2019 coronavirus disease (COVID-19) pandemic and associated lockdown periods had a significant negative impact on the wellbeing and quality of life of students. The negative impact of lockdown periods on health correlates such as immune fitness, alcohol consumption, and mood were reflected in their academic functioning. As both the duration and intensity of lockdown measures differed between countries, it is important to replicate these findings in different countries and cultures. Therefore, the purpose of the current study was to examine the impact of the COVID-19 pandemic on immune fitness, mood, academic functioning, sleep, smoking, alcohol consumption, healthy diet, and quality of life among Turkish students. Turkish students in the age range of 18 to 30 years old were invited to complete an online survey. Data were collected from n = 307 participants and included retrospective assessments for six time periods: (1) BP (before the COVID-19 pandemic, 1 January 2020–10 March 2020), (2) NL1 (the first no lockdown period, 11 March 2020–28 April 2021), (3) the lockdown period (29 April 2021–17 May 2021), (4) NL2 (the second no lockdown period, 18 May 2021–31 December 2021), (5) NL3 (the third no lockdown period, 1 January 2022–December 2022), and (6) for the past month. In this data descriptor article, the content of the survey and the dataset are described. Full article

The Bacillus thuringiensis serovar thuringiensis strain 800/15 has been actively used as an agent in biopreparations with high insecticidal activity against the larvae of the Colorado potato beetle Leptinotarsa decemlineata and gypsy moth Lymantria dispar. In the current study, we present the first draft genome of the 800/15 strain coupled with a comparative genomic analysis of its closest reference strains. The raw sequence data were obtained by Illumina technology on the HiSeq X platform and de novo assembled with the SPAdes v3.15.4 software. The genome reached 6,524,663 bp. in size and carried 6771 coding sequences, 3 of which represented loci encoding insecticidal toxins, namely, Spp1Aa1, Cry1Ab9, and Cry1Ba8 active against the orders Lepidoptera, Blattodea, Hemiptera, Diptera, and Coleoptera. We also revealed the biosynthetic gene clusters responsible for the synthesis of secondary metabolites, including fengycin, bacillibactin, and petrobactin with predicted antibacterial, fungicidal, and growth-promoting properties. Further comparative genomics suggested the strain is not enriched with genes linked with biological activities implying that agriculturally important properties rely more on the composition of loci rather than their abundance. The obtained genomic sequence of the strain with the experimental metadata could facilitate the computational prediction of bacterial isolates’ potency from genomic data. Full article

A registered trademark represents one of a company’s most valuable intellectual assets, acting as a safeguard against possible reputational damage and financial losses resulting from infringements of this intellectual property. To be registered, a mark must be unique and distinctive in relation to other trademarks which are already registered. In this paper, we describe the CMAD, an acronym for Conflicting Marks Archive Dataset. This dataset has been meticulously organized into pairs of marks (Number of pairs = 18,355) involved in copyright infringement across word, figurative and mixed marks. Organizations sought to register these marks with the National Institute of Industrial Property (INPI) in Brazil, and had their applications denied after analysis by intellectual property specialists. The robustness of this dataset is ensured by the intrinsic similarity of the conflicting marks, since the decisions were made by INPI specialists. This characteristic provides a reliable basis for the development and testing of tools designed to analyze similarity between marks, thus contributing to the evolution of practices and computer-based solutions in the field of intellectual property. Full article

China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km². The largest landslide occupies 2.95 km², while the smallest covers 84.47 m². Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km², the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km², and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km². This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts. Full article

Intracranial hemorrhage (ICH) is a dangerous life-threatening condition leading to disability. Timely and high-quality diagnosis plays a huge role in the course and outcome of this disease. The gold standard in determining ICH is computed tomography. This method requires a prompt involvement of highly qualified personnel, which is not always possible, for example, in case of a staff shortage or increased workload. In such a situation, every minute counts, and time can be lost. The solution to this problem seems to be a set of diagnostic decisions, including the use of artificial intelligence, which will help to identify patients with ICH in a timely manner and provide prompt and quality medical care. However, the main obstacle to the development of artificial intelligence is a lack of high-quality datasets for training and testing. In this paper, we present a dataset including 800 brain CT scans consisting of multiple series of DICOM images with and without signs of ICH, enriched with clinical and technical parameters, as well as the methodology of its generation utilizing natural language processing tools. The dataset is publicly available, which contributes to increased competition in the development of artificial intelligence systems and their advancement and quality improvement. Full article

Momentum has been a consistently studied aspect of sports science for decades. Among the established literature, there has, at times, been a discrepancy between conclusions. However, if momentum is indeed an actual phenomenon, it would affect all aspects of sports, from player evaluation to pre-game prediction and betting. Therefore, using momentum-based features that quantify a team’s linear trend of play, we develop a data pipeline that uses a small sample of recent games to assess teams’ quality of play and measure the predictive power of momentum-based features versus the predictive power of more traditional frequency-based features across several leagues using several machine learning techniques. More precisely, we use our pipeline to determine the differences in the predictive power of momentum-based features and standard statistical features for the National Hockey League (NHL), National Basketball Association (NBA), and five major first-division European football leagues. Our findings show little evidence that momentum has superior predictive power in the NBA. Still, we found some instances of the effects of momentum on the NHL that produced better pre-game predictors, whereas we view a similar trend in European football/soccer. Our results indicate that momentum-based features combined with frequency-based features could improve pre-game prediction models and that, in the future, momentum should be studied more from a feature/performance indicator point-of-view and less from the view of the dependence of sequential outcomes, thus attempting to distance momentum from the binary view of winning and losing. Full article

Organ-on-a-chip (OOC) technology has emerged as a groundbreaking approach for emulating the physiological environment, revolutionizing biomedical research, drug development, and personalized medicine. OOC platforms offer more physiologically relevant microenvironments, enabling real-time monitoring of tissue, to develop functional tissue models. Imaging methods are the most common approach for daily monitoring of tissue development. Image-based machine learning serves as a valuable tool for enhancing and monitoring OOC models in real-time. This involves the classification of images generated through microscopy contributing to the refinement of model performance. This paper presents an image dataset, containing cell images generated from OOC setup with different cell types. There are 3072 images generated by an automated brightfield microscopy setup. For some images, parameters such as cell type, seeding density, time after seeding and flow rate are provided. These parameters along with predefined criteria can contribute to the evaluation of image quality and identification of potential artifacts. This dataset can be used as a basis for training machine learning classifiers for automated data analysis generated from an OOC setup providing more reliable tissue models, automated decision-making processes within the OOC framework and efficient research in the future. Full article

Data breaches result in data loss, including personal, health, and financial information that are crucial, sensitive, and private. The breach is a security incident in which personal and sensitive data are exposed to unauthorized individuals, with the potential to incur several privacy concerns. As an example, the French newspaper Le Figaro breached approximately 7.4 billion records that included full names, passwords, and e-mail and physical addresses. To reduce the likelihood and impact of such breaches, it is fundamental to strengthen the security efforts against this type of incident and, for that, it is first necessary to identify patterns of its occurrence, primarily related to the number of data records leaked, the affected geographical region, and its regulatory aspects. To advance the discussion in this regard, we study a dataset comprising 428 worldwide data breaches between 2018 and 2019, providing a visualization of the related statistics, such as the most affected countries, the predominant economic sector targeted in different countries, and the median number of records leaked per incident in different countries, regions, and sectors. We then discuss the data protection regulation in effect in each country comprised in the dataset, correlating key elements of the legislation with the statistical findings. As a result, we have identified an extensive disclosure of medical records in India and government data in Brazil in the time range. Based on the analysis and visualization, we find some interesting insights that researchers seldom focus on before, and it is apparent that the real dangers of data leaks are beyond the ordinary imagination. Finally, this paper contributes to the discussion regarding data protection laws and compliance regarding data breaches, supporting, for example, the decision process of data storage location in the cloud. Full article

The quest for novel materials with extraordinary electronic and plasmonic properties is an ongoing pursuit in the field of materials science. The dataset provides the results of a computational study that used ab initio and semi-analytical computations to model freestanding nanosystems. We delve into the world of ribbon-like materials, specifically graphene nanoribbons, silicene nanoribbons, and germanene nanoribbons, comparing their electronic and plasmonic characteristics. Our research reveals a myriad of insights, from the tunability of band structures and the influence of an atomic number on electronic properties to the adaptability of nanoribbons for optoelectronic applications. Further, we uncover the promise of these materials for biosensing, demonstrating their plasmon frequency tunability based on charge density and Fermi velocity modification. Our findings not only expand the understanding of these quasi-1D materials but also open new avenues for the development of cutting-edge devices and technologies. This data presentation holds immense potential for future advancements in electronics, optics, and molecular sensing. Full article

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19. Full article

Although other methods exist to store and manage data in modern information technology, the standard solution is file systems. Therefore, keeping well-organized file structures and file system layouts can be key to a sustainable research data management infrastructure. However, file structures alone lack several important capabilities for FAIR data management: the two most significant being insufficient visualization of data and inadequate possibilities for searching and obtaining an overview. Research data management systems (RDMSs) can fill this gap, but many do not support the simultaneous use of the file system and RDMS. This simultaneous use can have many benefits, but keeping data in RDMS in synchrony with the file structure is challenging. Here, we present concepts that allow for keeping file structures and semantic data models (in RDMS) synchronous. Furthermore, we propose a specification in yaml format that allows for a structured and extensible declaration and implementation of a mapping between the file system and data models used in semantic research data management. Implementing these concepts will facilitate the re-use of specifications for multiple use cases. Furthermore, the specification can serve as a machine-readable and, at the same time, human-readable documentation of specific file system structures. We demonstrate our work using the Open Source RDMS LinkAhead (previously named “CaosDB”). Full article

This dataset aimed to explore associations between pre-SARS-CoV-2 infection exercise and sports-related physical activity (PA) levels and disease severity, along with treatments administered following the most recent SARS-CoV-2 infection. A comprehensive analysis investigated the relationships between PA categories (“Inactive”, “Low PA”, “Moderate PA”, “High PA”), disease severity (“Sporadic”, “Episodic”, “Recurrent”, “Frequent”, “Persistent”), and treatments post-SARS-CoV-2 infection (“No treatment”, “Home remedies”, “Prescribed medication”, “Hospital admission”, “Intensive care unit admission”) within a sample population (n = 5829) from the Hellenic territory. Utilizing the Active-Q questionnaire, data were collected from February to March 2023, capturing PA habits, participant characteristics, medical history, vaccination status, and illness experiences. Findings revealed an independent relationship between preinfection PA levels and disease severity (χ² = 9.097, df = 12, p = 0.695). Additionally, a statistical dependency emerged between PA levels and illness treatment categories (χ² = 39.362, df = 12, p < 0.001), particularly linking inactive PA with home remedies treatment. These results highlight the potential influence of preinfection PA on disease severity and treatment choices following SARS-CoV-2 infection. The dataset offers valuable insights into the interplay between PA, disease outcomes, and treatment decisions, aiding future research in shaping targeted interventions and public health strategies related to COVID-19 management. Full article

The infections caused by various bacterial pathogens both in clinical and community settings represent a significant threat to public healthcare worldwide. The growing resistance to antimicrobial drugs acquired by bacterial species causing healthcare-associated infections has already become a life-threatening danger noticed by the World Health Organization. Several groups or lineages of bacterial isolates, usually called ‘the clones of high risk’, often drive the spread of resistance within particular species. Thus, it is vitally important to reveal and track the spread of such clones and the mechanisms by which they acquire antibiotic resistance and enhance their survival skills. Currently, the analysis of whole-genome sequences for bacterial isolates of interest is increasingly used for these purposes, including epidemiological surveillance and the development of spread prevention measures. However, the availability and uniformity of the data derived from genomic sequences often represent a bottleneck for such investigations. With this dataset, we present the results of a genomic epidemiology analysis of 17,546 genomes of a dangerous bacterial pathogen, Acinetobacter baumannii. Important typing information, including multilocus sequence typing (MLST)-based sequence types (STs), intrinsic bla_OXA-51-like gene variants, capsular (KL) and oligosaccharide (OCL) types, CRISPR-Cas systems, and cgMLST profiles are presented, as well as the assignment of particular isolates to nine known international clones of high risk. The presence of antimicrobial resistance genes within the genomes is also reported. These data will be useful for researchers in the field of A. baumannii genomic epidemiology, resistance analysis, and prevention measure development. Full article

Audio-image representations for a multimodal human action (MHAiR) dataset contains six different image representations of the audio signals that capture the temporal dynamics of the actions in a very compact and informative way. The dataset was extracted from the audio recordings which were captured from an existing video dataset, i.e., UCF101. Each data sample captured a duration of approximately 10 s long, and the overall dataset was split into 4893 training samples and 1944 testing samples. The resulting feature sequences were then converted into images, which can be used for human action recognition and other related tasks. These images can be used as a benchmark dataset for evaluating the performance of machine learning models for human action recognition and related tasks. These audio-image representations could be suitable for a wide range of applications, such as surveillance, healthcare monitoring, and robotics. The dataset can also be used for transfer learning, where pre-trained models can be fine-tuned on a specific task using specific audio images. Thus, this dataset can facilitate the development of new techniques and approaches for improving the accuracy of human action-related tasks and also serve as a standard benchmark for testing the performance of different machine learning models and algorithms. Full article

Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. It is possible to think about feature selection as an optimization problem. Swarm intelligence algorithms are promising techniques for solving this problem. This research paper presents a hybrid approach for tackling the problem of feature selection. A filter method (chi-square) and two wrapper swarm intelligence algorithms (grey wolf optimization (GWO) and particle swarm optimization (PSO)) are used in two different techniques to improve feature selection accuracy and system execution time. The performance of the two phases of the proposed approach is assessed using two distinct datasets. The results show that PSOGWO yields a maximum accuracy boost of 95.3%, while chi2-PSOGWO yields a maximum accuracy improvement of 95.961% for feature selection. The experimental results show that the proposed approach performs better than the compared approaches. Full article

Legume plants enter a symbiosis with soil nitrogen-fixing bacteria (rhizobia), thereby gaining access to assimilable atmospheric nitrogen. Since this symbiosis is important for agriculture, biofertilizers with effective strains of rhizobia are created for crop legumes to increase their yield and minimize the amounts of mineral fertilizers required. In this work, we sequenced and characterized the genome of Rhizobium ruizarguesonis bv. viciae strain RCAM1022, a component of the ‘Rhizotorfin’ biofertilizer produced in Russia and used for pea (Pisum sativum L.). Full article

Appeals to governments for implementing basic income are contemporary. The theoretical backgrounds of the basic income notion only prescribe transferring equal amounts to individuals irrespective of their specific attributes. However, the most recent basic income initiatives all around the world are attached to certain rules with regard to the attributes of the households. This approach is facing significant challenges to appropriately recognize vulnerable groups. A possible alternative for setting rules with regard to the welfare attributes of the households is to employ artificial intelligence algorithms that can process unprecedented amounts of data. Can integrating machine learning change the future of basic income by predicting households vulnerable to future poverty? In this paper, we utilize multidimensional and longitudinal welfare data comprising one and a half million individuals’ data and a Bayesian beliefs network approach to examine the feasibility of predicting households’ vulnerability to future poverty based on the existing households’ welfare attributes. Full article