An Early Stage Researcher's Primer on Systems Medicine Terminology

The integration of data science into interdisciplinary research (IDR) has become a cornerstone in addressing complex global challenges, particularly in the digital era. In Vietnam, while there has been a growing recognition of data science's potential, its integration into interdisciplinary research remains in the nascent stages. This paper examines the current situation of interdisciplinary research with data science in Vietnamese universities, identifying key challenges and opportunities. The study highlights the importance of infrastructure development, human capital, and collaborative frameworks to promote data science across academic disciplines. It also provides strategic recommendations for fostering a data-driven research ecosystem, ensuring sustainable development and global competitiveness. The paper concludes by proposing actionable steps for Vietnam to establish itself as a leader in interdisciplinary data science research, contributing to solving pressing societal issues.

The confluence of science, technology, and medicine in our dynamic digital era has spawned new data applications to develop prescriptive analytics, to improve healthcare personalization and precision medicine, and to automate the reporting of health data for clinical decisions.1 Data science in health care has seen recent and rapid progress along 3 paths: (1) through big data via the aggregation of large and complex data sets including electronic medical records, social media, genomic databases, and digitized physiological data from wireless mobile health devices2; (2) through new open-access initiatives that seek to leverage the availability of clinical trial, research, and citizen science data sources for data sharing3; and (3) in analytic techniques particularly for big data, including machine learning and artificial intelligence that may enhance the analyses of both structured and unstructured data.4 As new data sets are created, analyzed, and become increasingly available, several key questions emerge including the following: What is the quality of unstructured data generation? Will the use of nonstandardized methods in data processing with traditional software and hardware lead to data fragmentation and analyses that are nonreproducible? Will healthcare systems incorporate and use big data especially from new publically and patient-generated sources? How will physicians and researchers learn from new open-sourced data and big-data analytics? And ultimately, How can they acquire the skills to create a knowledge translation in data sciences?5 Practicing in an era of continuous payment reform and decline in research funding, early career investigators are challenged to keep up with the accelerating pace of change in medicine, all while being expected to provide meaningful contributions through productive clinical, educational, and research experiences.6 In this perspective, we aim to highlight how data science can catalyze professional advancement and discuss the implications of big data, open access, …

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics. This volume bridges the gap between data science and interdisciplinary research, making it a valuable resource for researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.

Data can guide decision-making to improve the health of communities, but potential for use can only be realized if public health professionals have data science skills. However, not enough public health professionals possess the quantitative data skills to meet growing data science needs, including at the Centers for Disease Control and Prevention (CDC). The Data Science Upskilling (DSU) program increases data science literacy among staff and fellows working and training at CDC. The DSU program was established in 2019 as a team-based, project-driven, on-the-job applied upskilling program. Learners, within interdisciplinary teams, use curated learning resources to advance their CDC projects. The program has rapidly expanded from upskilling 13 teams of 31 learners during 2019-2020 to upskilling 36 teams of 143 learners during 2022-2023. All 2022-2023 cohort respondents to the end-of-project survey reported the program increased their data science knowledge. In addition, 90% agreed DSU improved their data science skills, 93% agreed it improved their confidence making data science decisions, and 96% agreed it improved their ability to perform data science work that benefits CDC. DSU is an innovative, inclusive, and successful approach to improving data science literacy at CDC. DSU may serve as an upskilling model for other organizations.

Filling global gaps in monitoring data with local knowledge

Objective To investigate the construction and practice of an integrated interdisciplinary course based on cases and its effect on the development of common competency in students majoring in clinical laboratory science. Methods 116 students majoring in clinical laboratory science were enrolled from class 2014 (control group, n=59) and class 2015 (experimental group, n=57) in the study. The integrated interdisciplinary course based on cases was taught in the experimental group, while the traditional courses were taught in the controls. At the end of the fourth semester, exam scores of pathophysiology of all the students were compared. Questionnaires for teaching effects were scored in the experimental group at the beginning and at the end of the experiment semester, respectively. The scores were processed with SPSS 19.0 for independent-samples t-test. The experimental group was further divided into two subgroups, experimental class 1 with the course taught as case-based learning (CBL) and experimental class 2 with the course taught as team-based learning (TBL). Comparison between the questionnaire scores and the patho-physiology scores were also performed in the two experimental classes. Results The pathophysiology scores of the experimental group (75.65±7.13) were significantly higher than those of the controls (69.94±9.77), P=0.000. The scores of the 15 indicators in the questionnaire for the experimental group were significantly higher at the end of the semester than those at the beginning of the semester (P<0.01), including scores of learning interest [(7.59±1.48) vs. (3.81±1.80)], connection among basic medicine courses [(8.19±1.22) vs. (4.07±1.57)], integration of basic and clinical medicine courses [(8.33±1.19) vs. (3.36±1.42)], problem analyzing ability[(8.10±1.17) vs. (3.64±1.44)], problem solving ability [(7.71±1.14) vs. (4.12±1.51)], self-learning ability [(8.07±1.55) vs. (3.52±1.54)], communication skills [(8.02±1.15) vs. (3.59±1.34)], teamwork [(7.55±1.17) vs. (3.19±1.38)], and critical thinking [(7.41±1.23) vs. (3.72±1.36)] at the end of the semester and at the beginning of the semester, respectively. Conclusion The integrated interdisciplinary course based on cases can promote the development of post competency among students majoring in clinical laboratory science during basic medical education. Key words: Case-based learning; Integrated interdisciplinary course; Basic medicine; Clinical laboratory science; Post competency

Data science is an interdisciplinary field that deals with a methodical approach to process large volumes of data both structured and unstructured in nature. The very objective is to analyze the data to uncover hidden patterns and extract actionable insights from the data for better managerial decision-making in an organization. Data science has been used in diverse areas such as business and finance, marketing, risk management, operations and planning, disease diagnosis and health care, agriculture, fraud detection, crime investigation, image and speech recognition, gaming, virtual reality, weather and environmental studies, space and defense applications to name a few. Data science is not an entirely new discipline; rather, it has evolved from the existing fields such as data mining and knowledge discovery, business intelligence, data analytics, machine learning, computer science, software engineering, mathematics and statistics, among others. It is an umbrella field to many such fields which make data processing more systematic than ever before and very useful for organizational decision-making. Data science has a lot of potentials to solve complex organizational problems effectively. With the growth of social media, Internet of Things, ubiquitous computing, connectivity, ambient intelligence and above all digital economy, the field of big data has emerged as an opportunity as well as a challenge for many organizations. While big data stores a lot of business opportunities, how to make it useful for the organization is rather challenging. In this context, embracing data science becomes more pertinent for the organization. With the advent of big data, the importance and popularity of data science is accelerating. This chapter will provide a compressive introduction to data science and big data analytics. It will elaborate on the data analytics life cycle. The chapter will delve into the theories and methods such as regression, classification, clustering and association rules used in data science. It will also introduce the relevant technologies such as MapReduce, NoSQL and popular tools such as Hadoop ecosystem. Finally, this chapter will conclude with research challenges in the field of data science and big data analytics.

Law and legal studies has been an exciting new field for data science applications whereas the technological advancement also has profound implications for legal practice. For example, the legal industry has accumulated a rich body of high quality texts, images and other digitised formats, which are ready to be further processed and analysed by data scientists. On the other hand, the increasing popularity of data science has been a genuine challenge to legal practitioners, regulators and even general public and has motivated a long-lasting debate in the academia focusing on issues such as privacy protection and algorithmic discrimination. This paper collects 1236 journal articles involving both law and data science from the platform Web of Science to understand the patterns and trends of this interdisciplinary research field in terms of English journal publications. We find a clear trend of increasing publication volume over time and a strong presence of high-impact law and political science journals. We then use the Latent Dirichlet Allocation (LDA) as a topic modelling method to classify the abstracts into four topics based on the coherence measure. The four topics identified confirm that both challenges and opportunities have been investigated in this interdisciplinary field and help offer directions for future research.

The first university-level library schools were opened during the last quarter of the 19th century. The number of such schools has gradually increased during the first half of the 20th century, especially after the Second World War, both in the USA and elsewhere. As information has gained further importance in scientific endeavors and social life, librarianship became a more interdisciplinary field and library schools were renamed as schools of library and information science/ information studies/ information management/information to better reflect the range of education provided. In this paper, we review the major developments in education for library and information science (LIS) and the impact of these developments on the curricula of LIS schools. We then review the programs and courses introduced by some LIS schools to address the data science and data curation issues. We also discuss some of the factors such as "data deluge" and "big data" that might have forced LIS schools to add such courses to their programs. We conclude by observing that "data" has already triggered some curriculum changes in a number of LIS schools in the USA and elsewhere as "Data Science" is becoming an interdisciplinary research field just as "Information Science" has once been (and still is).

According to Wikipedia, "Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining." The oil and gas industry is increasingly expanding its activities by moving into the Data Science and analytics space to increase efficiency, reduce costs, make better decisions and improve quality of technical products and services. Through the extraction of knowledge and insights from historical data, oil and gas companies can systematically process the huge data available to them using scientific methods and algorithms to identify trends for problem identification and optimization opportunities. The data processing can also be used to perform analytics to provide Descriptive, Diagnostic, predictive or Prescriptive solutions for value creation. For Chevron offshore and onshore non-rig wellwork, the existing methodology of planning and scheduling Non-Rig Workovers (NRWOs) for execution is a spreadsheet or a Project typically run on Microsoft applications or software. This process does not incorporate numerous factors that affect the value realization through executing the NRWO such as historical Data Analytics, predictions and several extreme constraints. The value in building a prioritized candidate selection schedule is allowing the business to shift to a data-driven model based from a method of simple basic programs with limited options and typically biased by human input. Historical data from various sources is being collected to provide an encompassing view of the NRWO prioritization, planning and scheduling environment. The scope of this study involves utilizing Data Science to generate solutions comprising of prioritized scheduled workovers that are optimized by various constraints to rank these workovers such as individual well Non-Rig workover cost per barrel. The approach can be replicated using other operational and well related constraints to generate alternative optimized rigless well prioritization solutions. The resulting wells will be gauged against established business drivers to develop an optimal prioritized solution which is then applied at the start of the business plan year to provide an optimized wellwork schedule for the planning year. Data Science applied to this project utilizes the various systems of records within the offshore and onshore fields such as Wellwork candidate listings and categorization database, project maturation database, cost schedules, possibility of success, reserves, production profiles, etc. The systems of records are then integrated through Data Science and prioritized by ranking the various parameters through automation based on constraints specified by customers. The long-term project will reduce NPT by 2-3% annually, save well work maturation recycle time, and increase efficiency in executing wellwork through an optimized schedule. Equivalent cost savings of between $650,000 and $1m was estimated for the initial pilot simulation run for the business planning cycle evaluated. The methodology applied in this study provides a multidiscipline and integrated approach to bridge the conventional optimization void of Data Science and the big data approach to make quicker non-rig well scheduling decisions.

Data science is an interdisciplinary field that uses statistical, mathematical, and computational techniques to analyse large datasets and draw meaningful insights. It has been applied to a wide range of topics in science and engineering, including physics. In physics, data science is used to analyse large datasets generated from experiments and simulations. This allows researchers to gain insight into physical phenomena, such as the behaviour of particles, forces, and fields. Data science techniques can also be used to develop models that can be used to predict future events and outcomes. In addition, data science can be used to visualize physical phenomena, such as the structure of the universe or the evolution of stars. Data science is an important tool for physicists, as it provides a powerful way to analyse and understand physical phenomena. By leveraging data science techniques, physicists can gain a deeper understanding of the laws of nature and make predictions about the future. Keyword :Data Science, Physics, Application, data science techniques.

Linear Algebra is the branch of mathematics that deals with scalars, vectors, and matrices. The main motivation behind developing linear algebra was to solve the system of linear equations. Linear equations represent the basic objects in geometry such as lines and planes. Thus solving the system of linear equations computes the intersection of planes and lines. It is applied in physics and engineering to model many natural phenomena and calculate their efficiency. Linear algebra also plays an important role in Data Science. Data Science is the interdisciplinary field that deals with extracting meaningful insights from data using machine learning algorithms and computational techniques. Linear Algebra provides a mathematical framework to represent , understand , manipulate and obtain information from the data.It is also used in machine learning algorithms for prediction , classification and optimization.Linear algebra is foundational to data science, providing the tools for working with data in high-dimensional spaces, making complex data transformations, and optimizing machine learning algorithms. Linear algebra techniques are used for dimension reduction and recommendation systems. Data presentation and manipulation using vectors and matrix operations in Linear algebra. Principal component analysis(PCA) is a widely used technique to reduce the dimension of the data which uses the eigenvalue and eigenvectors in Linear Algebra. Natural Language process ( NLP) is one of the important areas of Data science which focuses on human and computer interaction. Linear Algebra provides a platform to represent , analyze and transform textual information in NLP. NLP models use Linear Algebra to process and understand natural language efficiently. In Data science, image processing and computer vision is used to extract ,interpret and analyze information from visual data. Linear algebra plays a key role in image processing and computer vision systems to obtain information in visual data . This paper focuses on such applications of Linear algebra in Data Science especially in machine learning algorithms, NLP, image processing and computer vision . Key words: Linear Algebra, Data Science, Machine learning, Dimension reduction , image procession, computer vision

Digital business transformation and the 5.0 era of the industry have made data science develop rapidly, and it is much needed to capture the potential of big data as a basis for business decision-making. How the Faculty of Economics and Business integrates data science into the curriculum will be challenging, considering that data science requires an understanding of mathematics, statistics, and technology. This research attempts to define data science and design the integration of data science into economics and business. Through the literature review process, it can be understood that data science is a multidisciplinary science (Mathematics, statistics, and other sciences according to their respective fields). Based on this, integrating data science into economics and business can be carried out by designing a curriculum that considers conceptual aspects of theory and the technical continuity of the use of technology.

The promise of translational physiology

The promise of translational physiology.