Scientific Data Research Articles

Pattern Recognition of EFL University Students’ Online Behaviors through Data Science: Any Investment on English Language Components or Skills?

Artificial intelligence (AI) has played a crucial role in many fields of study, and its merits and demerits have been investigated by related scholars. However, it seems a somewhat gray area when implementing AI in teaching and learning. This could be mainly because AI developers know little about learning varied sciences and they also lack pedagogical knowledge for implementing AI in teaching (Luckin & Cukurova, 2019). Therefore, the current research aimed at gathering data regarding the online behaviors of 21 Iranian EFL university students through installing an application on their mobile phones for a period of eight days. Afterwards, the collected data were analyzed through Data Science methods and varied patterns of behavior were recognized. The results exhibited that messaging and social media applications are two great parts of the students’ life. Also, there existed some common patterns regarding the applications students owned and the amount of time they spent on them. As a small case in point, the results of the study cannot be generalized; however, the outcomes highlight the proof that in an EFL context, serious steps need to be adopted by course designers and stakeholders in order to cultivate a greater and purposeful use of online learning applications, websites etc. among EFL university students so as to enhance the teaching-learning process. This gains prominence because any AI-mediated learning approach is believed to enhance students’ second/foreign language motivation as well as self-regulation in learning. AI is actually promising a revolution in the realm of language education.

Biological Potential and Therapeutic Effectiveness of Hinokiflavone in Medicine: The Effective Components of Herbal Medicines for Treatment of Cancers and Associated Complications

Background: Plants have been providing us medicines and food material for centuries. Traditional system of medicine, including Ayurveda, and Traditional Chinese medicines have been playing important role in health sectors for the treatment of human disorders since very early age. Plant secondary metabolites, including flavonoids, coumarins, saponins, and tannins have significant therapeutic potential in medicine. Biflavonoids are dimers of flavonoids, linked by a C–O–C or C–C bond. Hinokiflavone is an important class of biflavonoids found to be present in Toxicodendron succedaneum, Isophysis tasmanica, Juniperus rigida, Juniperus phoenicea, Platycladi cacumen, Rhus succedanea, Selaginella tamariscina, Platycladus orientalis, Selaginella bryopteris, and Metasequoia glyptostroboides. Methods: Biological potential of hinokiflavone in medicine have been analyzed in the present work through scientific data analysis of various literature work. Scientific database, including Google, Scopus, Science Direct, and PubMed were searched to collect all the scientific information of the present work using terms flavonoid, biflavonoid, herbal medicine, and hinokiflavone. Pharmacological activities of hinokiflavone were analyzed in the present work in very detailed manner. Analytical data of hinokiflavone were collected and analyzed in present work in order to know the biological source of hinokiflavone. Results: Present work signified the biological importance of hinokiflavone against various types of cancerous disorders, including breast cancer, colorectal cancer, esophageal squamous cancer, adenocarcinoma, hepatocellular carcinoma, myeloid leukemia, and melanoma. Further, its antiinflammatory, hepatoprotective, anti-viral, and anti-oxidant potential were also discussed in the present work. Moreover, its biological potential against COVID-19 and hair loss, and procoagulant activity were also summarized in this paper. Analytical data on hinokiflavone signified the importance of various analytical techniques in the extraction, separation, and identification of hinokiflavone with their pharmacokinetic parameters. Conclusion: Present work signified the biological importance and therapeutic potential of hinokiflavone in medicine.

PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems

The bumpy road to conservation: challenges and opportunities in updating the Galapagos zoning system

In 2014, the Galapagos National Park Directorate, sponsored by various international non-governmental organizations, initiated a comprehensive marine and terrestrial spatial planning process to improve the management effectiveness of Galapagos’ protected areas. This effort aimed to reconfigure management areas by expanding or redistributing no-take zones to protect Key Biodiversity Areas and ensure the conservation of at least 30% of terrestrial and marine ecosystems in the Galapagos. However, this process encountered significant challenges, notably the erosion of fishers' trust following the implementation of a "Marine Sanctuary". This top-down decision resulted in socio-political tension, compelling the Ecuadorian government to delay the implementation of the new marine zoning until there was conclusive scientific data on its socio-economic impact on local small-scale fishers’ livelihoods. This paper examines the achievements and shortcomings of the Galapagos zoning update process, highlighting the successful integration of scientific zoning criteria and the enhanced protection of critical biodiversity areas, together with the reduced support from fishers and legal disputes. To move forward, we recommended a bottom-up approach centered on rebuilding trust among the fishing community via innovative engagement strategies and the introduction of market incentives to promote sustainable practices. Assessing the long-term impact of no-take zones is essential for refining management approaches. Additionally, the application of cutting-edge technology within an adaptive co-management model is recommended to improve fisheries and ecological data gathering and analysis, facilitating proactive and well-informed management decisions. Our suggestions aim to reconcile conservation objectives with the needs and aspirations of the local communities in this iconic archipelago.

Developing an Educational Chatbot for Scientific Data Management Course Using DialogFlow

Developing an Educational Chatbot for Scientific Data Management Course Using DialogFlow

Web mining and sentiment analysis of COVID-19 discourse in online forum communities

Recently, various discussions, solutions, data, and methods related to coronavirus disease 2019 (COVID-19) have been posted in online forum communities. Although a vast amount of posting on COVID-19 analytical projects are available in the online forum communities, much of them remain untapped due to limited overview and profiling that focuses on COVID-19 analytic techniques. Thus, it is quite challenging for information diggers and researchers to distinguish the recent trends and challenges of COVID-19 analytic for initiating different and critical studies to fight against the coronavirus. This paper presents the findings of a study that executed a web mining process on COVID-19 data analytical projects from the Stack Overflow and GitHub online community platforms for data scientists. This study provides an insight on what activities can be conducted by novice researchers and others who are interested in data analysis, especially in sentiment analysis. The classification results via Naïve Bayes (NB), support vector machine (SVM) and logistic regression (LR) have returned high accuracy, indicating that the constructed model is efficient in classifying the sentiment data of COVID-19. The findings reported in this paper not only enhance the understanding of COVID-19 related content and analysis but also provides promising framework that can be applied in diverse contexts and domains.

Photonic data analysis in 2050

Photonic data analysis is a field at the intersection of imaging, spectroscopy, machine learning, and computer science. The diversity of both data types and application scenarios requires flexibility in the methods applied, combining a full range of computational methods, from classical chemometric techniques to state-of-the-art deep learning solutions. Interdisciplinary and international collaborations are needed to accelerate the progress of photonic data science. An underlying data infrastructure and standardization will be needed to provide collaborative platforms for research on data comparability, enabling the integration of novel photonic techniques into routine applications. The increasing complexity of the questions being investigated requires the application of more sophisticated data-driven models, which may only be optimized for large data sets. Unfortunately, novel techniques in the early stages of development can rarely provide a variability of measured samples sufficient to build a generalizable complex model. To overcome this problem, state-of-the-art methods will emerge for working with extremely limited or unbalanced data, as well as for dealing with device-to-device variation. Further developments are also foreseen in computable artificial intelligence methods, which will allow the validation of models of any architecture by comparing them with the knowledge of the researchers.

Esophageal food bolus impaction in pediatric age.

Esophageal food impaction (EFI) is the sudden onset of dysphagia that occurs when a food bolus becomes lodged in the esophagus, requiring endoscopic removal. Scientific data on the prevalence and causes of EFI in children is lacking. The aim of this study was to provide further insights into EFI episodes in children. We have prospectively enrolled all children admitted for a first episode of EFI between March 2018 and March 2023. A fluoroscopic contrast study was performed in all patients to confirm the boluses and assess their position. Boluses were extracted by esophagogastroduodenoscopies, and esophageal biopsies were routinely obtained for histologic evaluation. Over the study period, 41 children were admitted for a first episode of food impaction. Drooling was the most commonly reported symptom. Half children experiencing a first episode of food bolus were diagnosed with EoE (20/41, 48.8%). Almost a fourth of the episodes subtended a different condition, such as esophageal anastomotic, peptic or congenital strictures, stricturing caustic esophagitis, esophageal duplication,and achalasia. In the last fourth of patients the cause of EFI was not identified and thus probably related to quick eating and inadequate chewing of food. Our study represents the largest known series of pediatric patients evaluated for food bolus impaction. Our main finding is the high frequency of EoE, which accounts for a half of EFI episodes in pediatric age, especially in older children. This finding highlights the importance of obtaining esophageal biopsies after the endoscopic bolus removal in children with EFI to provide a complete diagnostic evaluation.

Focus group interviews as part of a clinical research immersion experience

Background Research is an essential component of the dental profession and should be integrated into dental education and training curricula. Opportunities to participate in clinical research studies can enrich the learning experience of those involved. Methods This paper reports a new educational method utilized to enhance the experiences of novice members of a research study team in conducting Focus Group Interviews (FGIs) as part of a double-blind clinical trial exploring an oral probiotic intervention. Study team members participated in a series of in-person FGIs conducted at the beginning and toward the end of a University-based clinical trial, which took place from October 2021 to October 2022. Results Study team members consisting of faculty, staff, and pre-doctoral dental students help plan and conduct interviews, curate, and qualitatively analyze the data. Study team members acquired experience with regard to interpersonal communication skills, organization, research subject communication, the application of human subject protection, FGI methodology, data analysis, developing presentations, and preparing scientific data presentation and publications. Conclusion Exposing dental faculty and students to clinical research studies in university settings can offer opportunities for experiential, multidimensional learning and the development of new research knowledge and skills.

The tale of the sea turtle flipper tagging technique, a global environmental impact assessment

AbstractAlthough sea turtle flipper tagging is a tenet field technique generating irreplaceable scientific data worldwide since the inception of sea turtle conservation in 1920s along the Great Barrier Reef, the procedure was reported to trigger outbreaks of sea turtle fibropapillomatosis (FP) in 2018. This global environmental impact assessment performed during 2020–2023 meta‐analyzed three impacts on sea turtles health and conservation of the presence of visible flipper tags: a risk of clinical expression of FP lesions at the tag insertion point, a deterrent effect of the presence of flipper tags on sea turtle fishing, and a risk of entanglement of sea turtles in fishing gears. This essay concluded that the risk of expression of a FP lesion at the flipper tag insertion point is most probably correlated to a direct inoculation of ChHV5 via infected tagging equipment, and that the risk of entanglement in fishing gears can be mitigated by the choice of metallic flipper tags over plastic flipper tags. This essay also strongly recalls the sea turtle flipper tagging technique represents a remedy to knowledge gaps relevant to climate velocity research and can be a deterrent on sea turtle fishing in a frame of strong partnerships with indigenous people, independently of communities' socio‐cultural backgrounds.

Therapeutic Potential of Dicentrine in Human Disorders: A Review On Medicinal Importance and Pharmacological Activities of Aporphine Alkaloid

Backgrounds: Plants have always been an important source of food and drugs, in addition to their application in medical and cosmetic sectors. The popularity of herbal remedies has increased in many countries in recent decades. Herbal medicines contain numerous active phytocomponents with different biological activities. Alkaloid class phytochemicals have diverse chemical structures and pharmacological activities in nature. Additionally, Aporphinoids are an important class of plant secondary metabolites that have been used for the treatment of numerous human disorders for a long time in traditional medicine. Dicentrine is an aporphine class phytochemical isolated from numerous medicinal plants, including Actinodaphne sesquipedalis and Lindera megaphylla. Methods: The biological potential of dicentrine, an aporphine alkaloid derivative, has been described in the present work. Scientific data on dicentrine were collected here from different scientific databases and presented in this paper in order to know the biological importance of dicentrine in medicine for the treatment of human complications. Further, detailed pharmacological activities and scientific data on dicentrine were also analyzed in order to determine its therapeutic potential in medicine. Moreover, its analytical aspects were also described in this work to understand its separation and isolation methods. Results: The present work described the biological potential of dicentrine in medicine and its analytical aspects. It signified the biological potential of dicentrine in cancer, breast cancer, oral squamous cell carcinoma, lung adenocarcinoma, blood pressure, inflammatory disorders, hyperlipidaemia, arrhythmia, stomach muscle, and glomerulonephropathy. Further, its effectiveness in medicine was found to be mainly because of its antiplatelet, alpha 1-adrenoceptor, epidermal growth factor, antiprotozoal, larvicidal, antimicrobial, topoisomerase II, and acetylcholinesterase inhibitory potential. Moreover, other scientific data also signified its metabolism and pharmacokinetic parameters in terms of its analytical aspects in medicine. Conclusion: The present review gives us an updated summary of the scientific information for pharmacological activities and analytical aspects of dicentrine in medicine. It also signified the potential contribution of dicentrine in medicine for the development of a newer class of drug molecules for human disorders.

Gaining a better understanding of online polarization by approaching it as a dynamic process.

Polarization is often a cliché, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social networks, polarization cannot be viewed solely from an ideological or affective perspective. We propose to better understand the issue polarization by approaching it as a sequential process, drawing on a dual expertise in political and data sciences. We compare the polarization process between one mature debate (COVID-19 vaccine) and one emerging debate (Ukraine conflict) at the time of data collection. Both debates are studied on Twitter users, a highly politicized population, and on the French population to provide key elements beyond the traditional US context. This unprecedented analysis confirms that polarization varies over time, through a succession of specific periods, whose existence and duration depend on the maturity of the debate. Importantly, we highlight that polarization is paced by context-related events. Bearing this in mind, we pave the way for a new generation of personalized depolarization strategies, adapted to the context and maturity of debates.

Using human mobility data to detect evacuation patterns in hurricane Ian

ABSTRACT Hurricane Ian in 2022 was a destructive category 4 Atlantic hurricane striking the state of Florida, which caused hundreds of deaths and injuries, catastrophic property damage, and an economic loss of more than $112 billion. Before the landfall of Ian in Florida, the state government issued evacuation orders in high-risk zones to reduce casualties and injuries. However, there is limited data available to monitor the actual evacuation patterns and compliance with the evacuation orders at a large geographic scale. This study utilizes human mobility data (i.e. SafeGraph Weekly Pattern) to analyse the spatial patterns of evacuation during Hurricane Ian in 2022. The objectives of the study include three key aspects: 1) proposing an analytical workflow that utilizes human mobility data to detect mobility patterns in disasters and other emergency events; 2) identifying significant evacuation patterns, and 3) revealing the spatial variations in the compliance with evacuation orders in the affected areas. Using data science and spatial analysis techniques, this study detected notable changes in population movements, both within Florida and nationwide, which are potentially linked to the hurricane-induced population evacuation. The distance decay pattern of population flows from Florida demonstrates a propensity for individuals to relocate to nearby areas during the hurricane. Furthermore, the increase in population outflows from the impacted areas suggests the effectiveness of mandatory evacuation orders. A more pronounced increase in outflows from designated mandatory evacuation areas points to the public awareness of the evacuation zone designation. This study provides large-scale, fine-resolution analysis of evacuation behaviours in natural disasters which cannot be easily detected in traditional data sources. The analytical workflows provide actionable tools for government agencies and policymakers to evaluate the effectiveness of evacuation orders and improve evacuation plans in future disasters.

Databricks- Data Intelligence Platform for Advanced Data Architecture

Databricks, as a unified analytics platform, has emerged at the forefront of this evolution, offering scalable cloud-based solutions for data science and ML applications. This article explores the journey of Databricks in enabling data-driven decision-making through advanced analytics techniques. From its roots in Apache Spark to its current status as a leading platform for data engineering, data science, and machine learning, Databricks has continuously evolved to meet the growing demands of modern enterprises. This article examines the progression of data science/Machine Learning applications in Databricks, tracing their development from initial implementation to current state-of-the-art techniques and integration within the platform. Initially, the article delineates the inception of Databricks, focusing on its architecture and the early adoption of Apache Spark for big data processing. It explores how the platform's native support for various programming languages and its unified analytics engine facilitated the early stages of intelligent application development. The article further discusses the implications of these advancements for the future of data science and Intelligence within Databricks and the broader analytics ecosystem. It highlights the potential for further integration of AI and ML technologies, such as automated machine learning (AutoML) and real-time analytics, in enhancing decision-making processes and operational efficiencies across industries. The evolution of data science in Databricks has played a pivotal role in advancing big data analytics, offering scalable, efficient, and user-friendly solutions. This study not only charts the historical development of these applications within Databricks but also provides insights into future trends and potential areas for innovation. As data continues to grow in volume and complexity, platforms like Databricks will be instrumental in harnessing the power of data science and ML to drive insights and value across sectors.

Between control and participation: The politics of algorithmic management

AbstractUnderstanding the role of human management is crucial for the debate over algorithmic management—to date limited to studies on the platform economy. This qualitative case study in logistics reconstructs the actor constellations (managers, engineers, data scientists and workers) and negotiation processes in different phases of algorithmic management. It offers two major contributions to the literature: (1) a process model distinguishing three phases: goal formation, data production and data analysis, which is used to analyse (2) the politics of algorithmic management in conventional workplaces, which differ significantly from platform companies. The article goes beyond surveillance to elucidate the role of the regulatory framework, various actors' knowledge contributions to the algorithmic management system, and the power relations resulting therefrom. While the managerial goals in the examined case were not oriented towards a surveillance regime, the outcome was nevertheless a centralisation of knowledge and disempowerment of workers.

Objective metrics for ethical AI: a systematic literature review

AbstractThe field of AI Ethics has recently gained considerable attention, yet much of the existing academic research lacks practical and objective contributions for the development of ethical AI systems. This systematic literature review aims to identify and map objective metrics documented in literature between January 2018 and June 2023, specifically focusing on the ethical principles outlined in the Ethics Guidelines for Trustworthy AI. The review was based on 66 articles retrieved from the Scopus and World of Science databases. The articles were categorized based on their alignment with seven ethical principles: Human Agency and Oversight, Technical Robustness and Safety, Privacy and Data Governance, Transparency, Diversity, Non-Discrimination and Fairness, Societal and Environmental Well-being, and Accountability. Of the identified articles, only a minority presented objective metrics to assess AI ethics, with the majority being purely theoretical works. Moreover, existing metrics are primarily concentrating on Diversity, Non-Discrimination and Fairness, with a clear under-representation of the remaining principles. This lack of practical contributions makes it difficult for Data Scientists to devise systems that can be deemed Ethical, or to monitor the alignment of existing systems with current guidelines and legislation. With this work, we lay out the current panorama concerning objective metrics to quantify AI Ethics in Data Science and highlight the areas in which future developments are needed to align Data Science projects with the human values widely posited in the literature.

Virtual tomography as a novel method for segmenting machining process phases with the use of machine learning-supported measurement

A new idea of machine learning-based technological process segmentation with the use of multi-sensor measurement is proposed in this article. The proposed segmentation of the machining process through appropriate measurement data modelling provides valuable insight that is necessary for technological parameters optimization or predictive maintenance. By combining multi-sensor industrial measurement and data science, this new solution is a more advanced and effective way of determining qualitative characteristics of the machining process that must be taken into account when developing smart analytical approaches, such as digital twins. An experimental measuring system consisting of accelerometers and current transducers is described. The system is implemented on an industrial CNC machine in order to assess operating conditions of the spindle and axis drives during the milling process. A novel method of detecting and segmenting the milling phases is proposed, involving data preprocessing and time-series signals data analysis to detect specific patterns or features that are indicative of each phase of the milling process. When applied in smart structures or digital twins, the proposed segmentation method provides similar information to that obtained by tomography imaging; therefore, the new method is called as virtual tomography. The developed phase segmentation method is of vital practical importance in terms of industrial implementation of technological process measurement and diagnostic systems. Especially as it can provide valuable information concerning elementary cutting zones and their influence on the process efficiency or influence of the composite structure layers machining on the tool wear, not available using hitherto known methods.

Ketogenic diet for ovarian disorders - promising nutritional approach in polycystic ovarian syndrome and ovarian cancer

Introduction and purpose:
 Polycystic ovary syndrome (PCOS) is the most common endocrine disease in the female reproductive age. As treatment guidelines include lifestyle changes, various diet types are being evaluated in terms of efficacy among them ketogenic diet (KD) which was also assessed in terms of its influence on another ovarian related disease - ovarian cancer. The aim of this paper is an analysis of the available scientific data in respect of the role of the KD as a part of the therapy of ovarian related disorders. The search was conducted using PubMed and Google Scholarship databases.
 Brief description of the state of knowledge:
 Ketogenic diet demonstrates favorable effects in the hormonal profile of  PCOS-affected women. It was also noticed that its administration results in the reduction of BMI and the improvement in glycemic parameters. Most of the research was conducted by linking the ketogenic diet with the caloric restriction. In the matter of ovarian cancer the results are unclear; some of the research states the improvement of the quality of life of the participants, but there is some alarming data of the possible role of KD in enhancing the tumor growth.
 Conclusions:
 Ketogenic diet merits further research as a possible factor in the treatment of PCOS as foregoing results are pointing to its efficacy. Nevertheless, still more data is needed to determine its role as studies were conducted within a short period of time with relatively small research groups. There are also other limitations such as the lack of comparison with other dietary patterns with the caloric deficit which makes it difficult to deduce the main profitable factor. When it comes to ovarian cancer the results of the current data are also unclear and unambiguous with the possible negative effects.

Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science

Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains.

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.